Moving object detection and tracking is the process of identifying and locating the class objects such as people, vehicle, toy, and human faces in the video sequences more precisely without background disturbances. It is the first and foremost step in any kind of video analytics applications, and it is greatly influencing the high-level abstractions such as classification and tracking. Traditional methods are easily affected by the background disturbances and achieve poor results. With the advent of deep learning, it is possible to improve the results with high level features. The deep learning model helps to get more useful insights about the events in the real world. This chapter introduces the deep convolutional neural network and reviews the deep learning models used for moving object detection. This chapter also discusses the parameters involved and metrics used to assess the performance of moving object detection in deep learning model. Finally, the chapter is concluded with possible recommendations for the benefit of research community.
Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications
