Integrating crowd simulation for pedestrian tracking in a multi-camera system

Multi-camera multi-target tracking is one of the most active research topics in computer vision. However, many challenges remain to achieve robust performance in real-world video networks. In this paper we extend the state-of-the-art single camera tracking method, with both detection and crowd simulation, to a multiple camera tracking approach that exploits crowd simulation and uses principal axis-based integration. The experiments are conducted on PETS 2009 data set and the performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) based on the position of each pedestrian on the ground plane. It is demonstrated that the information from crowd simulation can provide significant advantage for tracking multiple pedestrians through multiple cameras.