A graph-based MAP solution for multi-person tracking using multi-camera systems

Accurate multi-person tracking under complex conditions is an important topic in computer vision with various application scenarios such as visual surveillance. Taking into account the difficulties caused by 2D occlusions, missing detections, and false positives, we propose a two-stage graph-based object tracking-by-detection approach using multiple calibrated cameras. Firstly, data association is formulated into a maximum a posteriori (MAP) problem. After transformation, we show that this single MAP problem is equivalent of finding min-cost paths in a two-stage directed acyclic graph. The first graph aims to extract an optimal set of tracklets based on the hypotheses on the ground plane by using both 2D appearance feature and 3D spatial distances. Subsequently, the tracklets are linked into complete tracks in the second graph utilizing spatial and temporal distances. This results in a global optimization over all the 2D detections obtained from multiple cameras. Finally, the experimental results on three difficult sequences of the PETS´09 dataset with comparison to the state-of-the-art methods show the precision and consistency of our approach.