Design parameters and levels.
\r\n\tEven though video surveillance systems have been part an integral part of the public and security sectors for decades, there is a significant interest in them outside of those industries. This interest is largely due to increased crime rates and security threats all around the globe, which are driving a continuous growth of the video surveillance market. According to a recent report, the video surveillance market was valued at $29.98 billion in 2016 and is expected to reach a value of $72.19 billion by 2022. This market potential is also propelled by recent advances in Artificial Intelligence and Computer Vision research fields—boosting the intelligence, scalability, and accuracy of intelligent video surveillance solutions.
\r\n\r\n\tThe book's goal is to provide a game-changing and cross-disciplinary forum that brings together experts from academia, industry, and government to advance the frontiers of theories, methods, systems, and applications.
",isbn:"978-1-80356-342-8",printIsbn:"978-1-80356-341-1",pdfIsbn:"978-1-80356-343-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"4d13a124dd9eb965b2e6958786b710cb",bookSignature:"Dr. Pier Luigi Mazzeo",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11548.jpg",keywords:"Hardware and Software Architectures, Privacy in Surveillance, Cybersecurity for Surveillance, Biometrics, Activity and Interaction Analysis, Cognitive Dynamic Systems and Bio-Inspired Methods, Human-Computer Interfaces, Visualization Algorithms, Classification and Recognition, Sensors, Communications and Networked Sensing, Distributed Camera Networks and Smart Cameras",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 17th 2022",dateEndSecondStepPublish:"March 17th 2022",dateEndThirdStepPublish:"May 16th 2022",dateEndFourthStepPublish:"August 4th 2022",dateEndFifthStepPublish:"October 3rd 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Artificial Intelligence and Computer Vision enthusiastic researcher at Institute of Applied Science and Intelligent Systems in Lecce (Italy) with more than one hundred publications in his referred research fields.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",middleName:null,surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo",profilePictureURL:"https://mts.intechopen.com/storage/users/17191/images/system/17191.jpeg",biography:"Pier Luigi Mazzeo obtained an MSc in Computer Science from the University of Salento, Lecce, Italy, in 2001. Since then, he has been working on several research topics regarding artificial intelligence and computer vision. Dr. Mazzeo joined the Italian National Research Council of Italy (CNR) as a researcher\nin 2002. He is currently involved in projects for algorithms for video object tracking, face detection and recognition, facial expression recognition, deep neural networks, and machine learning. He has authored and co-authored 100 publications, including more than fifteen papers published in international journals and book chapters. He has also co-authored five national and international patents. Dr. Mazzeo acts as a reviewer for several international journals and for some book publishers. He has been regularly invited to take part in the scientific committees of national and international conferences.",institutionString:"Italian National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444315",firstName:"Karla",lastName:"Skuliber",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/444315/images/20013_n.jpg",email:"karla@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"8725",title:"Visual Object Tracking with Deep Neural Networks",subtitle:null,isOpenForSubmission:!1,hash:"e0ba384ed4b4e61f042d5147c97ab168",slug:"visual-object-tracking-with-deep-neural-networks",bookSignature:"Pier Luigi Mazzeo, Srinivasan Ramakrishnan and Paolo Spagnolo",coverURL:"https://cdn.intechopen.com/books/images_new/8725.jpg",editedByType:"Edited by",editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10390",title:"Deep Learning Applications",subtitle:null,isOpenForSubmission:!1,hash:"5cc6cd7972551be6cfc4d3c87bf8fb5c",slug:"deep-learning-applications",bookSignature:"Pier Luigi Mazzeo and Paolo Spagnolo",coverURL:"https://cdn.intechopen.com/books/images_new/10390.jpg",editedByType:"Edited by",editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53737",title:"Direct-Contact Heat Exchanger",doi:"10.5772/66630",slug:"direct-contact-heat-exchanger",body:'\nDirect transfer involves two immiscible fluids under different temperatures in contact for heat exchange [1]. Compared with the traditional direct-contact heat exchanger, heat transfer means has more advantages due to a more simple design, low temperature driving force and higher heat transfer efficiency [2, 3]. Direct-contact heat exchangers (DCHEs) make use of gas-liquid phase change heat exchanger within the working fluid. That is to say, DCHEs put to use heat transfer between two kinds of fluid in the absence of a partition. A direct contact heat exchanger can be used for seawater desalination, heat recovery, ocean thermal energy conversion, thermal energy storage systems, etc. [4, 5]. In addition, DCHEs have been applied to give a good solution in harnessing the solar energy [6] and provide a better understanding of ice formation, growth and detachment from the droplets producing ice slurry [7].
Mixing plays a fundamental role in many industrial applications, such as chemical engineering, metallurgical process, printing process, medical and bio-medical industries, and has a decisive impact on the overall performance of reaction processes. The purpose of mixing is to obtain a homogeneous mixture; however, many researchers have pointed out that the local mixing and the flow pattern has significant effects on the properties of the final products [8]. There is an increased want for measuring and comparing mixing performance. An efficient evaluation of mixing effects is required in those various fields, but as a result of its intricacy, theoretical methods are very limited. Monitoring or measuring the mixing appropriately is of much concern from the practical point of view and for the confirmation of theoretical models as much [9]. The existence of a second phase that makes the continuous phase flow and mixing process more complicated, especially for a direct contact with the boiling heat transfer process. The boiling heat transfer process, in which mixing efficiency assessment is common, is one of the most efficient kinds of heat transfer processes widely used in numerous engineering systems. Hence, the work of characterizing the homogeneous bubbling regimes in a DCHE is one of the most useful and instructive topics in DCHE.
There are two basic types of bubbling regimes in DCHE: homogeneous and heterogeneous. In the homogeneous bubbling regime, there are few diversifications in the size of the bubbles, and breakage and coalescence phenomena are inappreciable [10–12]. Industrially, nevertheless, the homogeneous bubbling regime is not likely to prevail, thanks to the high gas flow rates used. This is good for the heterogeneous bubbling regime, characterized by a widespread of bubble sizes and crucial frequencies of breakage and coalescence [13]. For an air-water system, Ribeiro and Lage [13] employed transient experimental measuring of the temperature of the liquid, bubbling height, evaporation rate, gas volume fraction and bubble size distributions in a direct-contact evaporator for four surface gas velocities including operation in both homogeneous and heterogeneous bubbling regimes. Ribeiro et al. [14] also analysed the photographs of homogeneous and heterogeneous bubbling regimes using different liquids in a DCHE handling with a perforated-plate sparger. Le Coënt et al. [15] studied the compounding of two staves and a viscous liquid in a classical reactor. He found that there was an alleged “pseudo-homogeneous” state before it was mixed completely homogeneously. In reality, a pseudo-homogeneity was achieved much more quickly (<40 s), but subsequent images revealed that polymers still remained in the reactor. The time of the pseudo-homogeneous state begins is called the pseudo-homogeneous time. In our DCHE, we found that there was a comparatively stable state in the completely heterogeneous bubbling regimes also. Consequently, we defined this completely stable state as pseudo-homogeneous. Peyghambarzadeh et al. [16] found that bubble growth was a considerably complicated process, and detecting distinguishable bubbles was scarcely possible at high heat fluxes, while in this experiment, we have captured the rough sketch of bubbles.
A literature survey showed that image analysis has been used in transparent laboratory vessels to circumvent the drawback of subjectivity of measurement interpretation. Fortunately, the image processing technology has been widely used for feature extraction in medical and chemical industries. Thus, just that technology of image intensification, these bubble images can be computed with the following methods. Bubble growth is severely a function of flow of heat and liquid flow rate [16]. If the flow rate is lower, larger bubbles are observed at constant heat fluxes. This may be due to the fact that the growth of bubbles weakens with the time which is necessary at the velocity of flow is higher. Hence, the bubbles are smaller than those observed at higher high velocity. Similarly, according to the results of Ref. [16], the effect of heat flux is more meaningful than that of flow velocity. Many small bubbles are created on the heat transfer surface, inventing high turbulence flow at high heat fluxes. Consequently, heterogeneous and pseudo-homogeneous bubbling regimes are necessary and worth learning in a DCHE. At the meantime, it is one of the most challenging tasks of direct-contact heat transfer. The current commonly used method is to do with image processing techniques to acquire the features of bubbling regimes.
In 1995, Hyde et al. [17] recommended the topological invariant features the topology penetration structure complexity, in the number of micro-structure processing is one of the two material phases. From the perspective of theory, Betti numbers are the number of handles a special case of a topological invariants in a micro-structure [18]. Algebraic topology provides measurable information on complex objects, and Betti numbers are rough measures of this information. Gameiro et al. [18] came up with a method using the Betti numbers to describe the geometry of the fine-grained and snake-like micro-structures created in the process of spinodal decomposition. The zeroth Betti numbers
In our previous work, using the Betti numbers for gas-liquid-solid three-phase mixing effects of molten salt system based on the reaction of CH4 + ZnO were characterized. Nitrogen was used to imitate the gas phase (CH4) and mainly mixing effect in the sink. The zeroth Betti numbers were used to measure the number of pieces in the patterns, bring about beneficial parameter to describe the mixture homogeneity, which was the number of masters in the micro-structure occupied by one of the two phases. The first Betti numbers were introduced to describe the mixing heterogeneity of mixture. Because we only quantified the solid-liquid mixed flow pattern, the mixture of nitrogen bubble will disappear after image binarization.
It must be pointed out that Gulawani et al. [20] studied and founded that the turbulent flow pattern in a gas-liquid interface heat transfer coefficient and the immersed surface has a significant impact. Under Gulawani et al. [20, 21] inspiration and guidance, our work is mainly described the flow pattern characteristics of bubbles under the effect of heat transfer in the DCHE. Dahikar et al. [22] and Tayler et al. [23] used the Betti numbers to represent the heterogeneous and pseudo-homogeneous of bubbles. In addition, the relationship between the Betti numbers and the heat transfer coefficient has been obtained in a DCHE.
Both mixing speed and phase transition time in the direct contact boiling heat transfer process are fast. An accurate mixing time is critical to appropriately evaluate computational fluid dynamics models and then enhances equipment understanding and develops scale down models for process characterization and design space definition during late stage process development. In the past few years, many researchers have studied the mixing time and many methods were proposed to measure mixing time. But at present, there is no generally accepted method of measuring mixing time, mainly because of each method is not universal, that is each method has its own limitations, such as conductivity [24], pH [25], the dual indicator system method [26], tracer concentration [27–30], electrical resistance tomography [9], coloration decolouration methods [31], the box counting with erosions method [15] and Betti numbers with image analysis [32]. The limitation of each method has been described in details [31]. In all of the above-mentioned technologies, the Betti numbers are one of the most worthy methods to measure the mixing time and get further information of the mixing process. The Betti numbers can be effectively quantitative mixing time, the development process of mixing and degree of homogeneity. It has been used to characterize the evolution of the bubble group in direct contact with the boiling heat transfer process. But, we found that the Betti number method to be used for mixing time and the different evaluation indexes for mixing time have a similar trend, such as the slope
The mixing process in DCHE has been studied by many experiments. Similarly, at present, there is no generally accepted way to measure mixing homogeneity, mainly because each method has its own deficiencies, such as thermal method, conduct metric method, pH method, decolourization methods, Schlieren method, Betti numbers method [36], etc. In all of the above-mentioned technologies, the Betti numbers have been used to characterize the evolution of the heterogeneous and pseudo-homogeneous bubbling regimes. But, with the Betti numbers for characterization of mixing uniformity have a space-time limitation; it may lead to significant errors in the evaluation of mixing uniformity. The key question is how to measure the random bubble swarm of minimum difference of space-time consistency bubble swarm of domain. Fang and Wang putted forward the concept of UD (uniform design) that dispersed experimental points uniformly scattered on the domain. One should choose a set of given all possible designs with amount of minimum difference of laboratories under the design of all possible factors and experimental runs. The above is the basic idea of UD [37]. UD has been widely used since 1980 [38]. Inspired and motivated by Fang [37–39], our main research objects are the study of characteristics of time-space features and analyse the mixing process of numerical simulation and experimental analysis. Uniform design theory and image analysis have been applied to quantitative uniformity of time and space in a DCHE.
Recently, we were vitalized and motivated by Xu et al. [39], by the literature that introduces the relatively not complicated and accurately uniformity coefficient (UC) technology, which is based on image processing technology and the theory of uniform design to determine the mixing time and uniformity in a DCHE. The space-time characteristics can be quantified by means of the uniformity coefficient method, which based on
The chapter is organized as follows. In the next section, experiments and methodology are presented; the results and discussion are presented subsequently; the conclusion is briefly summarized in this section finally. Then the acknowledgements and references are presented in the end.
The schematic of the experiment employed in the present research is sketched in Figure 1 [32]. There are two circulation loops in the test device for this experiment. The first loop, which consists of the DCHE (1), electric heater (2), heat transfer fluid (HTF), pump (7) and connecting inlet and outlet pipelines, is a continuous-phase circulation loop for fluid flow, and the other, which consists of the DCHE (1), centrifugal pump (4), plate condenser (5), centrifugal pump (6) and connecting inlet and outlet pipelines, is a dispersed-phase circulation loop for working medium flow. The temperature control device, gear oil pump (3), regulates the initial temperature difference arising from heat exchange. The frequency control cabinet, gas mass flow-meter (8), regulates the rates of flow of the HTF and working medium. The patterns were imaged by a high-speed shutter video camera, which was placed at the second viewing window. In the bubble evaporation process, we could observe the most active stage of the bubbling regime. HTF and the refrigerant R-245fa (1, 1, 1, 3, 3 pentafluoropropane) were used as the continuous phase and the dispersed phase in all runs, respectively.
Experimental equipment for direct-contact heat transfer.
The settings of the experimental plan affecting the heat transfer capacity of the tested DCHE are determined through the orthogonal array (OA) experimental design method.
\nAs listed in Table 1, design parameters with four factors and three levels were selected to investigate the influence of heat transfer capacity. The L9(34) orthogonal array table was chosen for designing the experiment. The interaction between the design parameters was neglected in the present study.
Symbol | Design parameter | Unit | Level | ||
---|---|---|---|---|---|
A | mm | 460 | 530 | 600 | |
B | Δ | K | 80 | 100 | 120 |
C | m3/s | 1 × 10−4 | 2 × 10−4 | 3 × 10−4 | |
D | kg/s | 0 | 0.15 | 0.3 |
Design parameters and levels.
As shown in Table 2, the numbers E1–E9 denote different experimental levels according to the orthogonal array table.
Experimental | E1 | E2 | E3 | E4 | E5 | E6 | E7 | E8 | E9 |
---|---|---|---|---|---|---|---|---|---|
460 | 460 | 460 | 530 | 530 | 530 | 600 | 600 | 600 | |
Δ | 80 | 100 | 120 | 80 | 100 | 120 | 80 | 100 | 120 |
1 | 2 | 3 | 2 | 3 | 1 | 3 | 1 | 2 | |
0 | 0.15 | 0.3 | 0.3 | 0 | 0.15 | 0.15 | 0.3 | 0 |
Design experiments according to four factors and three levels orthogonal table.
A high-speech video camera was employed to obtain the patterns, and the brand used was PRAKTICA from Germany, with resolution 4 million pixels with no LED light. The images, which were blurred in photographing, can be improved using some image processing techniques. It takes 8 minutes to shoot in each occasion of the orthogonal experiment. Because of difficulties in storing and calculating these images, we choose equal interval sampling from 6000 images, in total, 12,000 images are collected.
\nFigure 2 is randomly obtained in the present image-processing process. In order to suppress the background of the original image, eliminate noise and enhance the image, gray-scale transformation, top-hat transform is used here. The binaruzation operation was used to calculate the Betti numbers. With a dilation process, an erosion process named as an opening was executed. This process, aiming at removing tiny or isolate points at the finer locations, and smoothing the boundaries of larger points, could not change the size of the image significantly. In contrast, with a dilation erosion process, a dilation process named as an opening was executed. This operation, aiming at filling up tiny pores within the points, connecting nearby points, and smoothing the borders, could not alter the size of the image significantly. The opening is used here to remove small holes representing sile bubbles or small bubble swarms of the binarization images. Since the behavioural characteristics of bubble swarms could not be accurately portrayed by binarized images with noise, an opening operation must be executed to eliminate image noise by the appropriate thresholds selected.
Treatment for one piece of bubble swarm patterns.
Thus, the white area indicates the bubble swarm, and the black area refers to the continuous phase. As the experimental conditions, the captured image is relatively fuzzy; however, its quality can be improved by using the digital image processing techniques. The resultant image that could be used for the following analysis was identifiable.
Owing to the complexity of the DCHE multiphase structure, heat exchange performance has often been expressed in terms of the volumetric heat transfer coefficient,
where
where
where
Box-counting with erosions method, which was developed by Le Coënt et al. [15], can be applied to quantify the mixture homogeneity; however, it is not available for quantifying the mixture non-homogeneity. As shown in the experiment, some agglomerates still exist in the vessel after stirring for quite a long time. With computational homology, an original analysis method aiming at getting the quantification of the mixture homogeneity and non-homogeneity was proposed.
\nAs we all know that the zeroth Betti number and the first Betti number have the following information [18, 44]:
Set
\nwhere
In two-dimensional cases,
The calculation of Betti number is difficult, and the methods are only in their early stages [44]. The free software package CHomP was used to calculate Betti numbers [44, 45]. We could compute
Subsequently, we obtained the value of time
As Figure 3 shows, a conversion operation of open operation images was performed. The results showed that a black-and-white conversion directly leads to a switch between the corresponding objects of the zeroth and first Betti numbers [46]. To illustrate,
Influence of the boundary on the Betti numbers.
Let
Betti number histogram with a normal distribution fit.
Two consecutive points beyond the limits are viewed as exception criteria.
\nStep 1: Giving a time point
Step 2: Calculating the mean
Step 3: Determining whether an event exceeds the range of “
The technique by itself is not limited to transparent tanks. It can be used in conjunction with electrical resistance tomography (ERT), position emission tomography (PET) and magnetic resonance imaging (MRI) [36].
A popular figure of merit is the star discrepancy [48] and its generalization the
where
By taking
With the discrepancy criterion in mind, we next discuss how to construct a uniformity coefficient.
Definition 2.1. The local discrepancy function is
\nThe difference between theory and empirical distribution can be used to measure the local discrepancy function with a rectangle [
where
Definition 2.2. The mean absolute discrepancy is often defined as follows:
\nIn Figure 5, the influence of iteration steps on the measurement is not pronounced. The MAD (mean absolute discrepancy) is conducted by the four corners of an image.
Effect of initial positions on uniformity coefficient.
Definition 2.3. Uniformity coefficient (UC) at time
In every case, the degree of mixing uniform could be detected successfully by the uniformity coefficient method (Figure 6). After certain processing, the value range of UC is usually [0, 1]. We also denote that the measurement is not pronouncedly affected by the iterative steps.
Effect of expressions (left) and iteration steps (right) on uniformity coefficient.
In Figure 7, when the pixels sizes are reduced from 16:9 to 4:3, the influence of homogenization curve by the uniformity coefficient method is not reduced [40]. However, the trend of homogenization curve by Betti numbers method becomes unclear.
Effect of different pixels (16:9 and 4:3) on uniformity coefficient.
The evolution of the UC and Betti numbers of binary images at different image sizes was clearly shown in Figure 7.
\nQuasi-Monte Carlo method is the most commonly used measure of uniformity in the literature, especially when
where
According to Fang et al. [50], the reproducing kernel functions are taken, respectively, as follows,
\nhence, the analytical expressions for centred discrepancy and wrap-around discrepancy are as follows:
\nUC-CD and UC-WD related to time
where CD
Many bubble patterns are related to time
Step 1: Transform image matrix to 0–1 matrix.
\nStep 2: Search the coordinates of one bubble located in top-left and bottom-right corners.
\nStep 3: Calculate the mean values of rows and columns of the two above coordinates.
\nIn our work,
and where
The origin of coordinates lies in the bottom-left (BL) corner of one piece of pattern. Certainly, other three groups of transformation ways are used to make origin of coordinates locate in bottom-right (BR), top-right (TR) and top-left (TL) coiners’ of one piece of patterns, respectively. Detailed formulas as follows,
\nMore interesting, these transform methods are different but corresponding to the coordinates rotate operation for the rectangular plane coordinate system. Hence, we will talk about the rotational invariance and neglect the different transform methods in next section.
Now this new method is used to study the influence of the flow rate and the submerged length on the degree of the mixing homogeneity and non-homogeneity of solid and liquid. The acquisition system was shown in Figure 8. The patterns were gained at the speed of 30 frames per second by a camera taking 10,000 images in each experiment.
Scheme of experimental equipment.
Figure 9 shows that an initial image was subtracted from each image.
Binarization for one piece of images.
Figure 10 shows the evolution of
The evolution of Betti numbers at E4.
Experiments indicate that, in Figure 11, volumetric heat transfer coefficient shows good correlation with average Betti number and pseudo-average-time value [32]. An interesting tendency is found in the better cases of L6 and L2, in which the larger first Betti numbers averages and shorter pseudo-homogeneous times correspond to a higher volumetric heat transfer coefficient
Parameter | E1 | E2 | E3 | E4 | E5 | E6 | E7 | E8 | E9 |
---|---|---|---|---|---|---|---|---|---|
159 | 93 | 120 | 226 | 126 | 92 | 264 | 105 | 145 | |
197 | 186 | 208 | 177 | 197 | 189 | 187 | 176 | 196 | |
1.26 | 2 | 1.23 | 0.79 | 1.61 | 2.25 | 0.71 | 1.50 | 1.52 | |
0.96 | 1.21 | 0.86 | 0.83 | 1.20 | 1.44 | 0.75 | 1.11 | 1.19 |
The data of the
Fitting of
Let
(1) Mixing time estimations by different methods
\nBased on the above,
Evolution and determination of mixing time measured by two methods.
It is found that the correlation coefficient between
In Table 4,
Index | E1 | E2 | E3 | E4 | E5 | E6 | E7 | E8 | E9 |
---|---|---|---|---|---|---|---|---|---|
197 | 185 | 206 | 177 | 197 | 190 | 186 | 197 | 192 | |
159 | 93 | 120 | 226 | 126 | 92 | 264 | 105 | 145 | |
153 | 80 | 112 | 216 | 123 | 83 | 249 | 97 | 137 | |
135 | 71 | 158 | 177 | 134 | 101 | 232 | 99 | 133 | |
159 | 93 | 150 | 225 | 124 | 87 | 262 | 103 | 138 | |
159 | 99 | 165 | 215 | 115 | 79 | 252 | 85 | 139 | |
0 | −6 | −15 | 10 | 9 | 8 | 10 | 18 | −1 | |
1.24 | 1.99 | 1.72 | 0.78 | 1.56 | 2.07 | 0.70 | 1.88 | 1.32 | |
1.29 | 2.31 | 1.84 | 0.82 | 1.60 | 2.29 | 0.75 | 2.03 | 1.40 | |
1.46 | 2.61 | 1.30 | 1.00 | 1.47 | 1.88 | 0.80 | 1.99 | 1.44 | |
1.24 | 1.99 | 1.37 | 0.79 | 1.59 | 2.18 | 0.71 | 1.91 | 1.39 |
Computing results of mixing performance by four methods.
(2) Simulation experiments
\nBy real data analysis of the Bitti number data, we have compared the proposed method with mean method, slope method and SD method. In order to assess the effectiveness of the new method and provide more evidences of good performance of this method, the mean absolute error (MAE) and the mean square error (MSE) are often used.
\nwhere
From Table 5, we can see that proposed method has a distinct advantage [36]. Figure 13 shows an example of 1000 simulation results.
Index | Mean method | Slope method | SD method | 3σ method |
---|---|---|---|---|
MAE | 6.22 | 6.66 | 8.07 | 3.38 |
MSE | 41.78 | 44.85 | 101.85 | 12.11 |
Comparison of computer simulation results by 1000 times.
One of these simulation results by 1000 times.
(1) Quantification of mixing efficiency
\nThe variation of the uniformity coefficient with frames can be an effective method to determine the critical mixing time and mixing uniform.
\nIn Figure 14, quantitative comparisons of the homogenization curve and mixing time predicted by the uniformity coefficient method are conducted with reported experimental data and other predictions by the Betti numbers method.
Evolution of mixing efficiency by the uniformity coefficient method (denoted as U9) and Betti numbers method (denoted as B9).
The comparisons show that good agreements of the mixing time obtained by Betti numbers method and uniformity coefficient method have also been achieved as given in Table 6 [40].
Parameter | E1 | E2 | E3 | E4 | E5 | E6 | E7 | E8 | E9 |
---|---|---|---|---|---|---|---|---|---|
159 | 93 | 120 | 226 | 126 | 92 | 264 | 105 | 145 | |
151 | 97 | 172 | 224 | 120 | 84 | 259 | 118 | 127 |
Computing results of mixing time by the Betti numbers method (
(2) Recognition of local and global uniformity
\nThese plots of Figure 15 are got by simulation with 720 lines and 1280 rows. Among them, the simulation 1 (Figure 15a) is the corresponding local uniform description, simulation 2 (Figure 15b) corresponds to the portrayal of global uniform. The lattice points generate the example of lattice uniform used in the demonstration of the algorithm. Under the guidance of the lattice points method, the simulation 3 (Figure 15c) performs for a lattice points set, which has 234 bubbles. It is can be seen that the lattice uniform is most accurate uniformly. One can take set of points or objects, which are generated by these experiments and simulations to check the algorithm.
Numerical simulations and MAD evolutions of different mixing efficiency.
MAD evolutions of different experimental cases with the same Betti numbers.
(1) Video-frequency image sequence of experimental cases
\nIn Figure 17, quantitative comparisons of the homogenization curve utilizing uniformity coefficient with modified discrepancy methods are conducted with reported experimental data and the other method is UC-LD. The variation of the UC-LD and UC-WD versus
Uniformity coefficients of E2 and E7.
(2) Verification of properties
\nSuppose coordinates of bubble swarms in Figure 2 can be denoted as follows,
\nwhere
Modified UC | In proper order | Disordered | ||
---|---|---|---|---|
Bubble | Coordinates | Both | ||
UC-CD | 0.9289 | 0.9289 | 0.9289 | 0.9289 |
UC-WD | 0.9311 | 0.9311 | 0.9311 | 0.9311 |
The data of verification of invariance to permutation.
Modified UC | No reflected | Reflected | ||
---|---|---|---|---|
Both | ||||
UC-CD | 0.9289 | 0.9289 | 0.9289 | 0.9289 |
UC-WD | 0.9311 | 0.9311 | 0.9311 | 0.9311 |
The data of verification of invariance under reflection.
Modified UC | No projected | Projected to origin | ||
---|---|---|---|---|
UC-CD | 0.9289 | 0.3946 | 0.3936 | 0.0554 |
UC-WD | 0.9311 | 0.5263 | 0.5253 | 0.3128 |
The data of verification of projection uniformity.
(3) Time complexity. The time complexity of different methods is shown in Table 10. Through experimental comparison, we may draw the conclusion that UC-CD and UC-WD can replace UC-LD and Betti numbers to some extent. Determination of the position of bubble swarms spends too much time, which leads to make the upper time complicated. But, other progressive technology can change this disadvantage.
Single image | Betti numbers | UC-LD | UC-CD | UC-WD |
---|---|---|---|---|
Reckoning | 0.9632 | 0.9289 | 0.9311 | |
Running time (s) | 3.07 | 2.61 | 18.33 | 18.23 |
The data of time of different methods.
(4) Numerical simulations and experimental examples. In order to assess the performance of UC-CD and UC-WD implementation for approximating the discrepancy of a given set of points, the three sets in Figure 14 were used. Table 11 shows that UC-LD of the three simulated images are affected by initial position, but UC-CD and UC-WD not. Comparing the modified UC of Figure 14b and c, the absolute difference |0.9751−0.9657| = 0.0094 is less than |0.9623−0.9464| = 0.0159 since Figure 14b and c seems to have the same degree of mixing uniformity. So it is concluded that UC-CD may outperform UC-WD and perform more sensitive for practical engineering application in some sense. The data in Table 11 also show that the difference of mixing uniformity coefficients including UC-LD, UC-CD and UC-WD with the same Betti numbers in Figure 16a, b, d and e. Meanwhile, it is noticed that different initial positions are response to different UC-LDs, which bring unreasonable and bias measurement of uniformity in practice. In other words, UC-LD may result in multiple values, but UC-CD and UC-WD do not have this problem. Moreover, the absolute difference of UC-CDs is larger than that of UC-WDs. The comparison result shows that UC-CD performs more sensitive than UC-WD in identifying the different patterns with the same Betti numbers. Those are the major of our presented work in this part.
Plots | UC-LD | UC-CD | UC-WD | ||||
---|---|---|---|---|---|---|---|
TL | BL | BR | TR | ||||
(a) | 0.9326 | 0.9033 | 0.9381 | 0.9189 | 0.8052 | 0.8493 | |
(b) | 0.9854 | 0.9866 | 0.9854 | 0.9955 | 0.9751 | 0.9623 | |
(c) | 0.9925 | 0.9932 | 0.9930 | 0.9925 | 0.9657 | 0.9464 | |
(a) | 0.8699 | 0.9077 | 0.9094 | 0.9033 | 0.9102 | 0.9170 | |
(b) | 0.9826 | 0.9523 | 0.9823 | 0.9869 | 0.9474 | 0.9436 | |
(d) | 0.8686 | 0.9507 | 0.8645 | 0.9056 | 0.8793 | 0.9121 | |
(e) | 0.9676 | 0.9794 | 0.9504 | 0.9825 | 0.9429 | 0.9306 |
The data of numerical simulations and experimental examples.
Because a new technique based on algebraic topology was introduced for quantifying the efficiency of multiphase mixing, the mixture homogeneity and the non-homogeneity of the mixture can be characterized by the Betti numbers for binary images of the patterns. The zeroth Betti numbers
In a DCHE, Betti number can estimate the number of bubbles assembling in flow patterns and to get the pseudo-homogeneous time. Experimental analysis constructs a simple linear model representing a bubble swarm and the heat transfer performance of a DCHE. In addition, the Betti number average and the pseudo-homogeneous time
A novel method relying on image analysis and statistics was developed to estimate the mixing time accurately in a DCHE. The three sigma method researches the critical point determination of the pseudo-homogeneous process, which satisfies approximately normal distribution and surpasses the range of occurring twice. The mean value method, slope method and standard deviation method make quantitative comparisons of the mixing time. In addition, time intervals between in-homogeneous time and mixing time quantify the quasi-steady state. Neglecting critical point could make substantial errors in mixing time estimation, which is proved.
A straightforward method, uniformity coefficient (UC) method based on
The properties of UC have been explored and there was a great influence of calculating the initial position on the original UC, namely UC-LD. The UC-LD method applies to the modified uniformity coefficient based on modified
In summary, we believe that on the basis of a large amount of previously published works, the complexity of the bubble swarm patterns can be reduced and their mechanisms clarified, and the heat transfer performance in a DCHE can be elucidated.
This work is supported by the National Natural Science Foundation of China (51666006, 51406071) and Scientific and Technological Leading Talent Projects in Yunnan Province (2015HA019).
Computer vision (CV) is a compelling field of Artificial intelligence that develops the theory and methods by which information about the real world can be automatically extracted and analyzed from image data. Image data can be in many forms, such as image, video, depth image, multi-camera views, or multidimensional data from a medical scanner.
The objective of CV is to model the real world or to recognize objects from digital images enabling computers or devices to “see”, interpret, manipulate, analyze, and understand what was seen and draw conclusions about the properties of the 3D world based on a given image or a sequence of images [1].
Basic CV tasks are image classification, segmentation, similarity calculation and object localization. Recognition of objects present on the scene and their features (e.g., shapes, textures, colors, sizes, spatial arrangement,) is often prerequisite for more complex CV tasks such as image retrieval, image description, object detection, object tracking, action recognition, image or scene analysis, and image understanding [2].
In all tasks, the starting point are the image features that carry important information and need to be extracted and processed in order to generate new information and conclusions. The image features can be divided into low-level features such as corners, edges, or contours that can be extracted with relatively simple image operations, and high-level features that require domain-knowledge to get structured information related to the object or action being taken [3].
Feature extraction can be described as a pre-processing step to remove redundant parts from the data and keeping the key information for accomplishing the task. Some well-known features that can be extracted are Optical flow for extracting motion information, Histogram of Oriented Gradients (HOG) and Silhouette for extracting shape information, Space–Time Interested Points for extracting interest points, etc.
To accomplish typical CV tasks, Image processing and Machine learning (ML) play an important role. Image processing is focused on low-level features and manipulation of image data for normalizing photometric properties of visual data, removing digital noise, data augmentation, etc., and is not concerned with understanding the content of visual data. However, when it comes to interpret the content and draw conclusions about the image to automate CV tasks, the most important fields are ML and its subfield Deep learning (DL) [2].
Before DL, computer vison tasks required a lot of coding and manual effort to define the features that can be extracted from images, with little automation involved [4]. With DL methods such as Convolutional Neural Network (CNN) [5], much of that work related the features to be extracted can be inferred automatically from data. Even though many features can be extracted automatically in the CNN framework for different tasks, manual feature extraction can still be useful for either augmenting the automatically extracted features or perform other tasks such as temporal segmentation of video or detection of active players.
Typical CV tasks such as object detection, object tracking, and action recognition, the tasks we will focus on the most here, are supervised learning tasks (Figure 1). Supervised learning relies on labeled ground truth data, based on which the learning algorithm infers the mapping between the raw data and the desired labels in the training stage. Thus, a prerequisite for supervised learning is data preparation, which includes data collection and labeling, pre-processing and feature extraction, followed by splitting data into a training and testing set, then selecting an appropriate learning algorithm and model structure for the specific task. After training and validating the model, the model needs to be tested and the obtained results need to be compared with the ground-truth data to evaluate the performance. The performance evaluation is represented with different metrics appropriate for the specific task.
Supervised learning process.
CV tasks can be implemented for image and video analysis in different domains, including the sports domain. Various CV techniques can be very useful for all parties interested in analyzing the game, including the coach, the reporters, the referee team, the physiotherapists and others, for making decisions about an occurred event, for monitoring and comparing the performance of each individual player, for choosing a strategy, for fast automatic analysis of video materials captured during a match or practice and the like.
In this chapter, the focus will be on handball, a team indoor sport played with a ball by two teams with seven players, one of whom is the goalkeeper. To analyze handball sports videos, different CV tasks can be combined. For example, the object or person detection can be applied to detect the players on the field, the object tracking to follow the players’ movements across the field, and action recognition to analysis of the players’ performances.
In the next sections, a created dataset for handball will be presented, and then the simple CNN architecture and typical measures for evaluation of model performance are described. In the following sections, CV tasks will be described and implemented in the context of handball. Object detection with YOLO and Mask R-CNN is presented in Section 5, object tracking with Hungarian algorithm and Deep SORT in Section 6, and action recognition using LSTM model in Section 7. Applications of optical flow and spatiotemporal interest points for temporal segmentation and active player determination are presented in Section 8.
The handball dataset used for the following experiments was recorded during a handball school, where participants were young handball players and their coaches.
The dataset consists of high-quality video recordings of practices and matches, filmed in a sport hall or in an outdoor handball field, without additional scene preparation or player instruction to preserve real-world conditions. The recordings were made using different stationary cameras positioned on the left or right border of the field on a tripod at 1.5 m, or from the spectator’s viewpoint at the height of approximately 3.5 m and the distance from the filed limit of 10 m. The recordings are in full HD resolution (1920x1080) and contain from 30 to 60 frames per second.
The dataset is quite challenging with a cluttered background, a variable number of players at different distance from the camera, who move fast and often change direction, are often occluded with another player, have jerseys of similar color to the background, etc.
The data needs to be prepared, processed and labeled for each specific task that will be considered here, so that domain- and task-specific models can be made by either training from scratch if there is sufficient data, or preferably, by tuning an existing model for a similar task using examples from the new domain.
For the player and ball detection task, 394 training and 27 validating images were extracted from the videos in the handball dataset and manually labeled, to form the PBD-Handball dataset [6].
To obtain the ground-truth data for the player tracking task, a subset of videos from the handball dataset was first processed using the YOLOv3 object detector, then with the DeepSORT tracker to bootstrap the annotation process, and lastly manually corrected. The total duration of the annotated dataset, named PT-Handball, prepared for this task is 6 min and 18 s [7].
For the action recognition task, parts of the videos containing the chosen actions were extracted from the whole handball dataset to get a subset of 2,991 short videos that were then labeled with one of the 10 action classes, or the Background class where action is not happening. This dataset is referred to as PAR_Handball.
Typical models used today for image classification and object detections tasks are based on Convolutional Neural Networks (CNNs), since they are adapted to solve the problems of high-dimensional inputs and inputs that have many features. The CNN network consists of a number of convolution layers, after which the network has been named, the activation and pooling layers, and one or more fully connected layers at the end of the network [8], (Figure 2).
Simplified CNN architecture.
The convolution layer refers to a mathematical operator defined over two functions with real value arguments that give a modified version of one of the two original functions. The layer takes a map of the features (or, in the first layer, the input image) and convolves it with a set of learned parameters resulting in a new two-dimensional feature map. The sets of learned parameters (weights and thresholds) are called filters or kernels. Each filter is a 2D square matrix, small in size compared to the image to which it is applied (equal depths as well as the input). The filter consists of real values that represent the weights that need to be learned, so that the output feature map contains useful information such as a particular shape, color, edge in order to give the network good results.
The pooling layer is usually inserted between two successive convolution layers to reduce a map resolution and increase spatial invariance or network insensitivity to minor shifts such as rotations, and translations of features in the image. The pooling layer also reduces memory requirements for network implementation. The most commonly used pooling methods are arithmetic mean and maximum, but several other pooling methods are also used in CNN architecture, such as Mixed Pooling, Stochastic Pooling, Spatial Pyramid Pooling and others [8].
The activation function defines the output of a node given an input or set of inputs. In its simplest form, this function is binary and represents the action potential of neurons by propagating the output value of the neuron or by stopping it. There is a broad range of univariate functions of linear combination of the input variables acting as CNN activation functions such as linear activation functions, jump functions, and sigmoidal functions. The jump and sigmoidal functions are a better choice for neural networks that perform classification tasks while linear functions are often used in output layers where unlimited output is required. Newer architectures use activation functions, typically Rectified Linear Unit (ReLU), behind each layer.
A fully connected layer is the last layer in the network. The name comes because of its configuration: all neurons are linked to all the outputs of the neurons in the previous layer. Fully connected layers can be viewed as special types of convolution layers where all feature maps and all filters are 1 x 1.
Network hyperparameters are all parameters needed by the network that have to be set before the network is provided with data for learning. The hyper-parameters in convolutional neural networks are the learning rate, the number of epochs, the number and kind of network layers, the activation function, the initialization weights, input pre-processing and the error function.
Selecting the structure of the CNN network for feature extraction plays a vital role in object detection because the number of parameters and types of layers directly affect the memory requirements, speed, and performance of the detector.
In this paper, two types of CNN-based networks, YOLO and Mask-RCNN, have been used for object detection, while for the task of action recognition, the CNN network is not used on its own, but it forms a part of the more advanced LSTM network as the feature extractor.
The performance of object detectors is usually evaluated in terms of accuracy, recall, precision, and F1 score [9], for a given confidence threshold. The same measures can also be used for evaluation of the action classification task.
The detections are deemed true positive when the intersection over union of (IoU) the detected bounding box and the ground truth box is greater than 0.5. The IoU measure is defined as the ratio of the intersection of the detected bounding box and the ground truth (GT) bounding box and their union, see Figure 3.
Visualization of intersection over union (IoU) criteria equal to or greater than 50%.
Since the confidence threshold controls the tradeoff between recall and precision, Average Precision (AP) measure is frequently used to evaluate the performance of the detectors. The AP is the area below the precision-recall curve which is calculated for every class by varying the confidence threshold. To get the mean Average Precision (mAP) value, mean of AP values of all classes is calculated.
Since there is no single measure that can uniquely describe the complex behavior of trackers, several measures are used to evaluate the tracking performance. These measures are the number of identity switches (ID), identification precision (IDP), identification recall (IDR) and the identification F1 (IDF1) measures [10].
An identity switch occurs when an object that was assigned an ID j in previous frames, gets a new id k, k ≠ j in a subsequent frame. The IDF1 measure focuses on how long a target is correctly identified, regardless of the number of mismatches. It is the ratio of correctly identified detections over the average number of ground-truth and computed detections.
The task of object detection is to find instances of real-world objects in images or videos. A detected object is typically marked with a bounding box and labeled with a corresponding class label and classification confidence value. Thus, object detection includes both the problems of finding the location of the object on the scene and of classification for predicting the class to which the object belongs to.
In case of player detection, the object detector should be able to overcome challenging conditions such as variable number of players, different player positions, varying distance of the player from the camera, the possibility of changing shape and appearance of players in time, presence of the blur of due to the speed of the movement, occlusion, shadows of artificial and external light, as well as cluttered background.
Nowadays, the focus in object detection is on CNNs that have been extended to be able to both detect and localize individual objects on the scene. In the following subsections, two different object detectors YOLO and Mask R-CNN are described with a corresponding experiment of player and ball detection.
YOLO is a detection algorithm based on a single-stage CNN architecture that can detect multiple objects in an image in real-time. The main idea is to predict bounding boxes and confidence values for grid cells into which an image or frame is divided. In the cases when an object is spread across more than one grid cell, the holder of its prediction will be the center cell.
There have been four versions of YOLO since it was first published. In the original version, the network architecture has 24 convolutional layers with two additional fully connected layers. The purpose of the convolutional layers is the feature extraction, while for fully connected layers to calculate the bounding boxes predictions and probabilities. The bounding box predictions and class probabilities are associated with grid cells so that if an object occupies more than one cell, the center cell will be designated to be the holder of prediction for a particular object [11].
In the next version, YOLOv2 [12], five convolution layers were replaced with max-pooling layers, and instead of the fully connected layers, predefined anchor boxes are introduced. In the training phase, to define the anchor boxes, YOLOv2 uses k-means clustering on ground-truth bounding boxes where boxes translations are relative to a grid cell.
YOLOv3 [13] is the third version of the YOLO object detector. It consists of 53 convolutional layers of (3 × 3) and (1 × 1) filters with shortcut connections between layers (residual blocks) used for feature extraction. The last convolutional layer predicts the bounding boxes, the confidence scores, and the prediction class. It predicts possible bounding boxes at three different scales using a structure that is similar to feature pyramid networks. In this way three sets of boxes are predicted at each feature map cell for each scale, to improve the detection of objects of different sizes.
Player and ball detection performance of the YOLOv3 detector was tested on the handball dataset using two different models. The reference model, further marked as Y, is the pre-trained YOLOv3 model with 608 x 608 input image size with weights pre-trained on the COCO dataset and no additional training. The pre-trained model contains the person and sports ball among other classes from the COCO dataset. Transfer learning [14] was used to avoid training the models from the beginning.
The second model (YBP) was trained using transfer learning, on PBD-Handball part of the dataset. The input image resolution was increased to 1024 x 1024 from 608 x 608 of the original model and the model was trained for approximately 80 epochs. Figure 4 shows an example of detection results for the “person” class.
Player detections in handball scene with YOLOv3 (bounding boxes with confidences).
To evaluate the performance of a model, the average precision (AP) metric of both classes and the mean average precision are used and shown on Table 1.
PBD-Handball | |||
---|---|---|---|
Model | Ball AP | Person AP | mAP |
Y | 13.53 | 66.13 | 39.83 |
YPB | 35.44 | 63.77 | 49.61 |
Evaluation of the object detector.
The best results for ball detection in terms of AP were achieved with the YPB model, which was trained on additional examples for both ball and person class and had an increased input image size. A small amount of training data can significantly improve detection results as can be seen in the example of ball detection which improved for 23%. The achieved results are satisfactory given the demanding environment but are not sufficient for commercial application, so the training dataset should be increased.
Mask R-CNN [15] is a two-stage CNN that can not only detect and localize multiple objects simultaneously present in the image, but also provides a segmentation mask of the objects, that is, assigns a membership value to each of the pixels belonging to the object. The first stage of the network is a region proposal network that finds the regions of the image that are likely to contain objects (regions of interest, RoI) and proposes candidate object bounding boxes. A sliding window is applied to the feature map to examine the probability whether there is an object class or a background in the examined region. Then, bounding boxes and masks are generated with the corresponding confidence values for all possible classes.
In the second stage, there are two parallel branches of the network, a fully convolutional branch for predicting the segmentation masks and a fully connected branch used on each RoI for classification and for adjusting the proposed box size.
There are similar networks like R-CNN, Fast R-CNN, Faster R-CNN [16] on which Mask R-CNN is based to look up for object detection purpose.
The performance of the Mask R-CNN for player detection was tested on the PBD-Handball dataset using the standard Resnet-101-FPN network configuration with pre-trained parameters on the COCO dataset. For player detection experiment, only the bounding boxes that refer to the “person” class were considered.
To obtain a good balance of high detection rates and low false positive detections, detections with confidence values below a threshold experimentally set to 0.55 were discarded. The detector performance was evaluated in terms of recall, precision, F1 scores and inference time per frame (using the NVIDIA 1080ti GPU). The results and comparison with the YOLOv3 detector are shown in Table 2. Detection was considered as true positive when the intersection of the detected bounding box and the ground truth box was above 50%.
Object Detector/Measure | Inference time / frame | Recall | Precision | F1 |
---|---|---|---|---|
YOLOv3 | 0.04 s | 68% | 95% | 79% |
Mask R-CNN | 0.3 s | 76% | 98% | 85% |
Results of player detection with mask R-CNN and YOLOv3.
One handball scene with the bounding boxes, class confidence value, and segmentation masks obtained with Mask R-CNN is shown on Figure 5.
Player detections obtained with mask R-CNN in a handball scene (bounding boxes with segmentation mask and confidence value).
It can be concluded that the results of both the YoloV3 and Mask R-CNN detector are good enough to be used for further analysis of player performance. However, the YOLOv3 detector is much faster, so it can be used not only for offline analysis of recordings, but also for real-time detection, at the cost of somewhat reduced recall. The detection results could be improved if more data is used, but the performance depends on the number and size of the players on the scene, the contrast between a player and a background, illumination, etc.
Tracking of handball players in video is an example of a Multi-Object Tracking (MOT) problem, where the goal is to track both the position and the identity of multiple objects present in video, so that the same unique ID is assigned to each object in every frame it appears. In an ideal case, in every video frame, all the present players should be detected in their correct position and a unique ID for each player, that stays the same throughout the video, should be assigned. This is a difficult task as many players can be on the field, from 14 to 25, depending if it is a practice or a match, and every one of them needs to be tracked. Furthermore, players can leave and re-enter the camera field of view, move very quickly, often change directions, occlude each other and wear similar clothes, or clothes with similar color as the background [17].
Thanks to improvements in performance of object detectors, and thanks to the ability to deal with challenges such as cluttered scenes or dynamics of tracked objects, tracking-by-detection has become a leading paradigm for MOT.
When using tracking-by-detection, the tracking algorithm relies on the object detector to detect and locate the objects on the scene in each frame, while the role of the tracking algorithm itself is reduced to the problem of associating the detections across frames that belong to the same object. To do so, the tracker may use the information about bounding boxes obtained by object detection, such as their dimensions, the locations of their centroids, the relative position to the boxes in previous frames, or some visual features extracted from the image.
The Hungarian algorithm [18] solves the problem of finding the globally optimal assignment of IDs to detected player bounding boxes, with respect to some cost function that is defined for an individual assignment. Here the cost function is defined only in terms of the parameters of the bounding boxes detected by the object detector in the current and previous frame, without using any visual features extracted from the video frames. Its value depends on the Euclidean distance of each detected object’s bounding box centroid from the predicted centroid of an object in the track, and on the size difference of the bounding box and the last assigned bounding box to the same track.
Formally, the assignment cost
where
For the prediction of the centroid location
Moreover, a unique track ID is assigned to each detected bounding box whose detector confidence is higher than a set threshold during the initial assignment of bounding boxes to tracks. Afterwards, whenever the number of detected objects exceeds the number of currently active tracks, new tracks are created and initialized using the unassigned object’s bounding box.
An existing track is considered inactive when no detections are assigned to that track for several frames. Once a track is marked inactive, no further detections are added to it, so if an object later reappears or is detected again, it will get a new track ID and will be considered a new object.
Deep SORT [19] is a tracking algorithm that builds upon the Hungarian algorithm, adding the appearance information about the tracked objects into consideration when associating new detections with previously tracked objects. The appearance information is particularly useful for re-identifying players that were occluded or have temporarily left the scene. As in the previous case, a unique track ID is assigned to each bounding box within the first frame, and the Hungarian algorithm is used to assign the new detections to existing tracks so that the assignment cost function reaches the global minimum.
The cost function consists of the spatial distance
where λ is a tunable parameter that determines the relative influence of the spatial distance
The spatial distance
where
The visual distance
where
The
The appearance descriptors are extracted using a wide residual neural network comprising two convolutional layers followed by six residual blocks that output a 128-element vector, and then normalized to fit within a unit hypersphere so that the cosine distance can be used. The network was pre-trained on a person re-identification dataset of more than a million images of 1261 pedestrians. The appearance information helps with re-identification of objects that have not been tracked for some time because of missed detections, because they were under occlusion or because they have briefly left the scene.
New tracks are formed whenever there are more detections in a frame than there are existing tracks or when a detection cannot be assigned to any track, because its spatial or visual distance is too far from any existing track. The maximum allowed
The tracking of players previously detected with the YOLOv3 detector using pre-trained tiny-yolo weights and confidence threshold set to 0.5 was performed on the PT-Handball dataset with the Hungarian algorithm and Deep SORT [20].
An example of a tracking situation in sequential frames when occlusions occurred is shown in Figure 6. The numbers above the bounding boxes represent the tracking ID of each player. The shown situation is quite demanding, resulting in rather unstable and inconsistent tracking in the selected frame. The Hungarian algorithm successfully tracked one of four players, and two of the players got new IDs after occlusion, while one player has switched ID with another, so that 807 got previously existing ID 812 (Figure 6, top row). Deep SORT managed to track correctly all four players (Figure 6, bottom row).
An example of tracking situation with occlusion. Top: Hungarian algorithm, bottom: Deep SORT. The left and right frames are 1 second apart.
Since the best results were obtained with the Deep SORT algorithm, its ability to assign the correct IDs to detections is analyzed in more detail using the common MOT evaluation measures. The results are shown in Table 3.
Measure | Value |
---|---|
#tracks in the ground truth | 279 |
#tracks | 1554 |
Identity switches | 1483 |
IDF1 | 24.7% |
Performance evaluation of deep SORT.
For each player that should be tracked, the identity switches caused, 5 to 6 additional tracks on average, so there are 5 times more tracks than in ground-truth annotated data. Furthermore, a large number, precisely 1483, of identity switches are present, due to a relatively large number of players in the video that move fast, exit the camera field view, frequently change positions, and occlude each other.
The number of players that are simultaneously present in the frame obviously affects the tracking performance, and according to the IDF1 measure, the players can be correctly identified for 24,7% of the time.
Tracking mistakes can be attributed to several factors. As in all tracking-by-detection algorithms, the accuracy of tracking is greatly influenced by the accuracy of the object detector. If a player is inaccurately detected, the tracking will be inaccurate as well. Furthermore, the scale of an object, occlusion, and the similar color of the players’ clothes with the background often cause tracking problems. To overcome these problems, in further work, multiple camera systems will be investigated [21], which can also allow for a more robust generation of a top-view trajectory [22].
In Figure 7, the top row shows the problem of re-identification after occlusion, and the bottom row the problem of identity switch due to small scale and similar colors.
Problem of re-identification after occlusion.
The goal of action recognition is to infer which action takes place in a set of image or video observations. Some simple actions, like eating or cutting, could be recognized using just a single frame, but actions are mostly much more complex and take place over a period of time, so they need to be analyzed across consecutive video frames.
Here, for recognition of handball actions, a simple long short-term memory (LSTM)-based artificial recurrent neural network is used.
During the handball game, every player is moving around the field performing different actions with a ball, such as shot, jump-shot or dribbling, or without the ball, such as running or defending. Some actions are performed by more players such as passing the ball or crossing.
Unlike CNNs and other so-called feedforward neural (FFNN) networks, recurrent neural networks (RNNs) [23] have connections that feed the activations of an input in a previous time step back into the network, to influence the output for the current input. These activations from the previous time step are held potentially indefinitely in the internal state of the network, so the temporal context is not limited to a fixed window that could be used as an input to a FFNN. This property makes RNNs especially appropriate for modeling sequences, such as text or a sequence of video frames in action recognition.
Long Short-Term Memory (LSTM) [24] is a type of recurrent neural network (RNN) designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In the recurrent hidden layers, the LSTM contains special units called memory blocks. Those units contain memory cells, that have self-connections storing the temporal state of the network, and gates, which are special multiplicative units that control the flow of information. The flow of input activations into the memory cell is being controlled by the input gate, while the output gate controls the output flow of cell activations into the rest of the network. The forget gate scales the internal state of the cell before adding it as input to the cell through the self-recurrent connection of the cell, thus causing the adaptive forgetting or resetting the cell’s memory.
In the experiment handball actions from the PAR-Handball dataset are considered: Throw, Catch, Shot, Jump-shot, Running, Dribbling, Defense, Passing, Double-pass, and Crossing. An example of jump-shot action is shown in Figure 8. The action consists of a sequence of different phases of jump-shot action from running, take-off, flight, throw, and landing that are captured on different video frames.
Active player collage for jump-shoot action.
Different actions can take different amounts of time to perform, so the average number of frames in a video depends on the action class it belongs to, as shown in Figure 9.
Average number of frames per action from the handball dataset.
Only Throw and Catch actions are significantly shorter (two or three times) than the other action classes that have a duration of around 60 frames on average.
Because these two actions are also parts of the more complex ones, like Passing, the model is trained once with all 11 classes and once with 9 classes, excluding Throw and Catch.
The model selected for action recognition is a LSTM-based network with one LSTM layer with 1,024 units, followed by a dropout layer with 0.5 dropout, one fully connected layer with 512 neurons, also followed by a dropout layer with 0.5 dropout rate. and the output layer with 11 neurons.
The input to the LSTM consists of a sequence of features extracted from video frames using the InceptionV3 [25] network with the ImageNet [26] re-trained weights as the starting point. The model is trained with the Adam optimize with a learning rate of 0.00001 and decay of 10–6 for up to 100 epochs, stopping early if the validation loss does not improve for more than 20 epochs.
Different frame selection strategies and different input size of sequences from 20 to 80 were used to train the model, because actions might have most distinctive characteristics in different parts of the sequence.
In videos containing more frames than expected, the chosen number of frames were selected consecutively from either beginning, middle or the end of the video, or from the whole video by decimation, i.e., by skipping some frames at regular intervals. Conversely, copies of existing frames were inserted between frames to extend the number of frames.
The action recognition results obtained by the described LSTM model, considering the frame selection strategies and different class numbers are shown in Figure 10 in terms of validation accuracy.
Validation accuracy for different lengths of input sequences and 9 or 11 action classes.
Having to classify a smaller number of classes can generally be considered a simpler task, so, as expected, the models trained on 9 classes have better results on average than the models trained on 11 classes. However, the best result overall of 70.94% is obtained for the model with 11 classes and 45 frames taken from the middle of the sequences. This is possibly due to the overlap of some actions that make them more difficult to recognize, as the actions Throw, and Catch that are parts of other actions such as Passing, Double-pass, Crossing, Shot and Jump-shot. Closely behind with 70.55% is the model trained on 9 classes with 20 frames in the middle, and in the third place is the model trained on 9 classes with last 45 frames with 70.47% validation accuracy.
Taking into consideration only the number of input frames and ignoring the number of classes or frame selection, the best results are obtained with 45 frames followed by 20 frames. In most cases the additional frames in the sequence do not improve the result much over the models trained with 20 frames. Considering only the way the sequence is selected, the highest average accuracy of 67.69% is achieved by the model while trained on 9 classes and the last frames selection, followed by 67,05% by skipping frames.
It can be noted that regardless the strategy of selecting frames, increasing the number of input frames does not contribute to a better result. The number of frames and frame selection strategies appear to be highly dependent on the type of action being performed.
Low-level features extracted from video frames, combined with specific knowledge about the problem domain can sometimes be used for solving specific tasks and generate conclusions about the objects in the image or for scene analyzes. For example, optical flow can be used as a measure of motion in video, and for rough temporal segmentation of the input video in order to automatically cut periods of inactivity or detect intervals of repetition of a certain exercise in handball training.
If the low-level features such as optical flow or spatio-temporal interest points are used with additional information such as the detected player bounding boxes, conclusions can be drowned about the most active player on the scene and automatic detection of players that are at a certain time likely to be performing the action that is of most interest for the interpreting the scene [27].
A low-level feature that captures motion information is optical flow, which is estimated from the time-varying image intensity. A moving point on the image plane produces a 2D path
The movement of points can be estimated from consecutive video frames, using some optical flow estimation algorithm, e.g., the Lucas-Kanade method [28]. This method assumes that small sections, i.e., groups of pixels of the initial images move with the same velocity, so the result is a vector field V of velocities of each image section. At each point
A visualization of an optical flow field calculated between two video frames from the dataset is shown in Figure 11. The direction and magnitude of optical flow at each point is represented by the direction and length of each arrow.
Two consecutive frames in video and the corresponding optical flow field.
In an uninterrupted recording of a handball training, there are usually periods of repletion of a certain exercise, where all players repeat the exercise either simultaneously or taking turns, followed by short pauses where the coach explains the next exercise to be performed. The periods of higher activity, when players perform the exercises are characterized with the higher magnitude of extracted motion features from video, while the periods when players queue or wait for instruction (Figure 12) are characterized with lower magnitude of motion features [29].
A typical training situation. Two players on the right are performing the current task, while the rest are queuing.
To mark the periods of inactivity and segment the videos into sections where a single exercise is repeatedly practiced, an optical flow threshold is used. First, the optical flow field is calculated between two consecutive frames sampled each N frames (here, N = 50). Then, mean optical flow magnitude is calculated for each field, resulting in a single value for each sampled time point in video. The video is cut at time points when the mean magnitude of optical flow is lower than an experimentally determined threshold value. An example of the mean optical flow magnitude calculated for a video sequence where there were short pauses of 10–20 seconds between active repetition of an exercise was is shown in Figure 13. It can be seen that the normalized flow threshold of about 0.07 clearly separates the periods of inactivity from parts of video showing exercise.
An example of segmentation of a video sequence using optical flow magnitude.
In a typical footage of a handball game or training session, at a given time only one player or a small proportion of players present on the scene participate in the action that is currently in focus, e.g., jump-shot, passing, while others may perform actions that are not currently relevant for interpreting the situation, e.g., moving into their positions. To train the action recognition models, the actions of those players that perform the action of interest should be annotated. The annotation process is at least partly manual, so it is time-consuming and tedious given the large amounts of video data to process. To assist with annotation, a method is proposed to select among the detected and tracked players, the ones that are currently the most likely to be performing the action of interest, here called active players. Thus, instead of reviewing every single players’ activity at all times, the manual annotation is reduced to verification of only the proposed active players’ tracks.
First, the players are detected and tracked as described in previous chapters. Then, the information about player positions, i.e., the detected bounding boxes, is combined with the low-level movement features, such as optical flow or spatiotemporal interest points, to obtain a measure of each player’s activity in the considered time interval.
The optical flow-based measure of player’s activity (
An alternative feature to optical flow for defining the activity measure are spatiotemporal interest points, or STIPs. STIPs are an extension from the spatial domain into both spatial and temporal domain of the notion of interest points in images, which are points with a significant local variation of image intensities. STIPs are thus points in the image with large variation of values in both spatial and temporal directions around these points.
As for calculating the optical flow, there are several algorithms that can be used to detect the STIPs, e.g. the method presented in [30] is based on the Harris corner operator (Harris3D) that is extended into the spatiotemporal domain, [31] uses a Gaussian filter in the spatial domain and a Gabor band-pass filter in the temporal domain, the algorithm presented in [32] is based on Hessian3D derived from SURF, and the selective STIPs detector [33] is designed to specifically detect the STIPs that likely belong to persons and not to the background.
Given that movement during the performance of various sports actions causes significant variation of appearance in that part of image, it is expected that there will be more detected STIPs in image regions with more intense player’s activity [34].
An activity measure based on density of STIPs in the area near the detected player,
In the experiment here, the Harris3D detector with the default parameters was used to extract the STIPs. Figure 14 shows an example of the detected player bounding boxes and STIPs in a video frame.
Detected players and spatiotemporal interest points.
A threshold of activity measure can be used to filter active from inactive players, since the players that perform sports actions should make more sudden movements corresponding to higher activity measures than other players.
Looking at activity measures in a sequence of frames, the ranking of players’ activity can change between frames. So, to choose the players that are active throughout the sequence, the Active player score is calculated as the average activity measure of the player along the trajectory of the player’s bounding boxes. The result is a set of player trajectories with corresponding player activity scores (Figure 15).
Detected leading player (white box) and his trajectory through the whole sequence (yellow line).
In this chapter, the applications of deep learning methods on typical CV tasks such as object detection, object tracking and action recognition are presented on videos from the handball domain, recorded during training and matches.
Handball is a team sport, played with a ball, with well-defined player’s roles, goals and rules. During the game, the athletes move quickly throughout the field, change positions and roles from defensive to offensive and vice versa, use different techniques and actions, and doing so often get partially or completely occluded by another athlete, making player detection, tracking and action recognition challenging problems of ongoing research interest.
For detection, the algorithm must be able to locate an object in relation to its environment and, define that object. It is important for the detector to be as accurate and fast as possible especially if the real time detection is needed. State of the art deep learning-based detectors such as YOLOv3 and Mask R-CNN, prove to be successful for player detection, while the performance on ball detection still lags due to the combination of its small size, great speed and occlusion by the players.
Once objects such as players are detected, they can be tracked. Here, the Hungarian assignment algorithm and SORT with a deep association metric (Deep SORT) are considered for tracking. The goal of a tracker is to assign the same unique track ID to the same player in consecutive frames, which is complicated by the changes of appearance and sudden motions of players. Thus, the trackers can model this motion or the changing appearance to help the association process. The Deep SORT adds an appearance model based on deep neural network features. This appearance model allows the Deep SORT method to re-identify players that have been temporarily occluded or left the scene much more successfully than the other tested methods, making it more appropriate for use in the handball domain.
For the action recognition task, LSTM network is used, as it is suited to deal with both image information contained in a single video frame and its temporal evolution during the performance of actions. The obtained action recognition results are promising, however due to dependence of the action recognition model on the performance of previous stages, i.e. object detection and tracking, the challenge remains to improve all three tasks. As in all deep learning tasks, an important factor is gathering enough training data, which can be facilitated by methods that reduce the manual effort of labeling ground truth data. To that end, the experiments for automatic temporal segmentation of the raw footage and a method for detecting the active player in a sequence using low level visual features were presented.
Advances in deep learning methods promise continued improvement in the analysis of dynamic sports scenes, in order to recognize more complex activities, plan competitive tactics and monitor player progress.
This research was fully supported by the Croatian Science Foundation under the project IP-2016-2106-8345 “Automatic recognition of actions and activities in multimedia content from the sports domain” (RAASS) and by the University of Rijeka under the project number uniri-drustv-18-222.
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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. 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Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR"},{id:"25",title:"Evolutionary Computation",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization"},{id:"26",title:"Machine Learning and Data Mining",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence"},{id:"27",title:"Multi-Agent Systems",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. 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We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems.
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\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
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