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Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

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Abstract

Currently, many vision-based motion capture systems require passive markers attached to key locations 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.

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Jia Li, ChengKai Wan, DianYong Zhang, ZhenJiang Miao & BaoZong Yuan

Authors
  1. Jia Li
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  2. ChengKai Wan
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  3. DianYong Zhang
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  4. ZhenJiang Miao
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  5. BaoZong Yuan
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Corresponding authors

Correspondence to Jia Li or ZhenJiang Miao.

Additional information

Supported by the National Basic Research Program of China (Grant No. 2006CB303105)

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Li, J., Wan, C., Zhang, D. et al. Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints. Sci. China Ser. F-Inf. Sci. 52, 252–259 (2009). https://doi.org/10.1007/s11432-009-0040-x

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  • DOI: https://doi.org/10.1007/s11432-009-0040-x

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