Extraction of multiple motion trajectories in human motion

This paper presents a method for extracting multiple motion trajectories in human motions. We extract motion trajectories of body parts (hands and feet) using a new method based on optical flow information. This procedure is not sensitive to complicated backgrounds or color distribution of scenes. No body part model or skin color information is used in our method. We first detect Significant Motion Points (SMPs) and obtain motion trajectories by connecting related SMPs through frames using Modified Greedy Optimal Assignment (MGOA) tracker based on the distance, motion similarity, and optical flow information. We test our approach on actual ballet sequences from videos. The resulting trajectories can be used as potential features for activity recognition.