Co-operative Pedestrians Group Tracking in Crowded Scenes Using an MST Approach

We address the problem of multiple pedestrian tracking in crowded scenes in videos recorded by a static uncalibrated camera. We propose an online multiple pedestrian tracking algorithm that utilizes group behaviour of pedestrians using minimum spanning trees (MST). We first divide pedestrians into several groups using the agglomerative hierarchical clustering, taking position and velocity of pedestrians as features, and then we track each group, represented by an MST, with the pictorial structures method. We also propose a method to detect and handle interpedestrian occlusions using a custom trained head detector for crowded scenes. Finally, we present experiments on two challenging and publicly available datasets and show improvements on multiple object tracking accuracy (MOTA) over other methods.