About the book
Sensors use captured images to localize the target object's presence, orientation, and accuracy to the background. When training such models, including the image, a bound-box is provided so that the senor can learn different object locations. As the objects can appear in any area, single model accuracy is minimal. To improve the localization accuracy, one could use the multiple models and their combined prediction, to improve the overall accuracy. The localization algorithms determine the accuracy by calculating the overlap of the target with its ground-truth. If the overlap is more significant than 0.7, then the presence of an object is detected. Current large scale training in object localization in this area has annotated millions of objects with their bound-boxes to create the Common Object in Context image reference datasets. So detectors can be developed, but they need to augment the data necessary to improve the accuracy since the target, as mentioned, could occur anywhere, also their orientation could be different. Vision sensor localization algorithms need to be easily trained for varying target sizes, positions, and scales.