Multiple-target tracking in complex scenes is one of the most complicated problems in computer vision. Handling occlusions between objects is the key issue in multiple-target tracking. This paper introduces a method of motion segmentation into the object tracking system

and presents a SPA (skeleton points assign) based occlusion segmentation approach to track multiple people through complex situations which are captured by static monocular cameras. In the proposed method

we select the skeleton points and evaluate their occlusion states by bottom 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 is used to accomplish occlusion segmentation. Object tracking is handled by a particle filter-based tracking framework

and a probabilistic appearance model is used to find the best particle. Experiments are performed using the public challenging data set PETS 2009. The experimental results show that our approach can improve the performance of the existing tracking approach and handle dynamic occlusions better.