Single camera multi-person tracking based on crowd simulation

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 integration 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 additional prediction of individual positions, which is obtained from the crowd simulation algorithm. Our experimental 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 effectiveness of our approach.