Single Camera Multi-person Tracking Based on Crowd Simulation
Zhixing Jin, Bir Bhanu · 2015
Tracking individuals in video sequences, especially in crowded scenes, is still a challenging research topic in the area of pattern recognition and computer vision. However, current single camera tracking approaches are mostly based on visual features only. The novelty of the approach proposed in this paper is the integra-tion of evidences from a crowd simulation algorithm into a pure vision based method. Based on a state-of-the-art tracking-by-detection method, the integration is achieved by evaluating particle weights with addi-tional prediction of individual positions, which is ob-tained from the crowd simulation algorithm. Our exper-imental results indicate that, by integrating simulation, the multi-person tracking performance such as MOTP and MOTA can be increased by an average about 2% and 5%, which provides significant evidence for the ef-fectiveness of our approach. 1.