Ashwin Yadav

By Peeyush Awasthi, Ashwin Yadav, Naren Naik and Mudambi Ramaswamy Ananthasayanam

One of the well-known approaches to target tracking is the Kalman filter. The problem of applying the Kalman Filter in practice is that in the presence of unknown noise statistics, accurate results cannot be obtained. Hence the tuning of the noise covariances is of paramount importance in order to employ the filter. The difficulty involved with the tuning attracts the applicability of the concept of Constant Gain Kalman Filter (CGKF). It has been generally observed that after an initial transient the Kalman Filter gain and the State Error Covariance P settles down to steady state values. This encourages one to consider working directly with steady state or constant Kalman gain, rather than with error covariances in order to obtain efficient tracking. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. The current work first applies the CGKF concept to heterogeneous sensor based wireless sensor network (WSN) target tracking problem. The paper considers the Standard EKF and CGKF for tracking various manoeuvring targets using nonlinear state and measurement models. Based on the numerical studies it is clearly seen that the CGKF out performs the Standard EKF. To the best of our knowledge, such a comprehensive study of the CGKF has not been carried out in its application to diverse target tracking scenarios and data fusion aspects.

Part of the book: Adaptive Filtering

By Ashwin Yadav, Kamal Jain, Akshay Pandey, Joydeep Majumdar and Rohit Sahay

Surveillance cameras and sensors generate a large amount of data wherein there is scope for intelligent analysis of the video feed being received. The area is well researched but there are various challenges due to camera movement, jitter and noise. Change detection-based analysis of images is a fundamental step in the processing of the video feed, the challenge being determination of the exact point of change, enabling reduction in the time and effort in overall processing. It is a well-researched area; however, methodologies determining the exact point of change have not been explored fully. This area forms the focus of our current work. Most of the work till date in the area lies within the domain of applied methods to a pair or sequence of images. Our work focuses on application of change detection to a set of time-ordered images to identify the exact pair of bi-temporal images or video frames about the change point. We propose a metric to detect changes in time-ordered video frames in the form of rank-ordered threshold values using segmentation algorithms, subsequently determining the exact point of change. The results are applicable to general time-ordered set of images.

Part of the book: Intelligent Video Surveillance