A Rough Set Framework for Multihuman Tracking in Surveillance Video

Abstract

Multi-human tracking has become a key focus area in video surveillance applications. Several research works have been carried out in multi-human tracking in the past two decades. The techniques such as kalman filter, particle filter, markov chain process, blob detection, etc., have been used to detect and track humans. However, tracking the human objects in consecutive frames is still a challenging problem due to spatial disorder, non-linear motion and occlusion of human objects. Also, the human object labeling becomes difficult since most of the similarity measures used in the classification process do not consider the positional coordinates while computing the similarity. This paper addresses the above challenges by introducing a rough set framework which identifies the human objects using modified bounding box generation technique and by applying the rough set classifier. The framework was tested on the benchmark dataset PETS09 and the experimental results were compared with OCELM, MODT and MPTS algorithms. The comparative analysis shows that the rough set framework outperforms the existing algorithms in terms of detection and tracking accuracy even in the cases of overlapped and hidden human objects.