Extraction and temporal segmentation of multiple motion trajectories in human motion

Abstract

A new method for extraction and temporal segmentation of multiple motion trajectories in human motion is presented. The proposed method extracts motion trajectories generated by body parts without any initialization or any assumption on color distribution. Motion trajectories are very compact and representative features for activity recognition. Tracking human body parts (hands and feet) is inherently difficult because the body parts which generate most of the motion trajectories are relatively small compared to the human body. This problem is overcome by using a new motion segmentation method: at every frame, candidate motion locations are detected and set as significant motion points (SMPs). The motion trajectories are obtained by combining these SMPs and the color–optical flow based tracker results. These motion trajectories are inturn used as features for temporal segmentation of specific activities from continuous video sequences. The proposed approach is tested on actual ballet step sequences. Experimental results show that the proposed method can successfully extract and temporally segment multiple motion trajectories from human motion.