Multi-target tracking with occlusions via skeleton points assignment

Multiple-target tracking in complex scenes is one of the most complicated problems in computer vision. Handling the occlusion between objects is the key issue in multiple-target tracking. This paper introduces the method of motion segmentation into the object tracking system, and presents a SPA (Skeleton Points Assign, SPA) based occlusion segmentation approach to track multiple people through complex situations captured by static monocular cameras. In the proposed method, we first select the skeleton points and evaluate their occlusion states by low-level information like optical flow; then we assign these points to different objects using advanced semantic information, such as appearance, motion and color; finally, a dense classification of foreground pixels are taken advantages of to accomplish occlusion segmentation and a blob-based compensation strategy is utilized to estimate the missing information of occluded objects. Object tracking is handled by a particle filter-based tracking framework, in which a probabilistic appearance model is used to find the best particle. Experiments are conducted on the public challenging dataset PETS 2009. Results show that this approach can improve the performance of the existing tracking approach and handle dynamic occlusions better.