Visual tracking of human head and arms with a single camera

This paper presents an upper body tracking algorithm with a single monocular camera. In order to be suitable for human robot interaction, the designed method should be free to work on the moving camera platform and also can achieve real-time performance. The dimension of human posture model is extremely high, and we hereby focus on the visual extraction of head and arms. A hierarchical structure model is proposed to solve the tracking problem by particle filter with partitioned sampling in the order of head, upper arm and the forearm. The hand position, straight edge of arm and temporal information are combined by the multiple importance sampling particle filter to efficiently estimate the irregular gesture of arms on image frames. The visual clues of the motion, appearance and shape to human face and arms are to verify the various hypotheses from the multiple importance sampling schemes. To validate the effectiveness of the proposed tracking approach, extensive experiments have been performed, of which the results appear to be quite promising.