Currently, many vision-based motion capture systems require passive markers attached to key loca- tions on the human body. However, such systems are intrusive with limited application. The algorithm that we use for hu...Currently, many vision-based motion capture systems require passive markers attached to key loca- tions on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field （MRF） and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences.展开更多
基金Supported by the National Basic Research Program of China (Grant No.2006CB303105)
文摘Currently, many vision-based motion capture systems require passive markers attached to key loca- tions on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field （MRF） and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences.
