A Rough Set Framework for Multihuman Tracking in Surveillance Video

MultiHuman Tracking (MHT) has become a key focus area in video surveillance applications. Several research works have been carried out in MHT in the past two decades. The techniques such as the Kalman filter, particle filter, Markov chain process, blob detection, and so on have been used to detect and track humans. However, tracking human objects in consecutive frames is still a challenging problem due to spatial disorder, nonlinear motion, and occlusion of human objects. Also, 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 article addresses the above challenges by introducing a rough set framework that identifies the human objects using the 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 One-Class Extreme Learning Machine (OCELM), Multi-Object Detection and Tracking (MODT), and Multi-Person Tracking in Smart Surveillance System (MPTSSS) 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.