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Video Multiple Object Detection and Tracking (VMODT) is the current area of research in computer vision which has increased due to the attention of commercial and academic potential it has offered. Multiple Object Tracking (MOT) shares all challenges to be handled such that long time occluded, fully occluded in small object, frequent occlusion in crowd and severely blurred in fast motion in video etc., In this paper, a review on VMODT with various models,approaches and tracking algorithms is carried out. Neural Networks are developed to provide optimal solution in monitoring the regularity, re-identification, predicting the activity and control action of objects in surveillance application. This review work will be helpful for understanding the start-of-art in VMODT, finding the limitation in current algorithm. However, hybriding the different tracking methods will improve the performance and facilitate new approaches in deep learning.

A Review on Video Tracking for Multiple Objects in Neural Network