A Novel Human Pose Estimation Method with Bayesian Network
碩士 === 輔仁大學 === 電子工程學系 === 97 === Human pose estimation method is important for the development of behavior recognition, human-robot interaction and visual surveillance. Markerless human pose estimation method can provides non-intrusive and high-free motion capture. It has great challenges due to la...
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| Format: | Others |
| Language: | en_US |
| Published: |
2009
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| Online Access: | http://ndltd.ncl.edu.tw/handle/83487486874454086698 |
| Summary: | 碩士 === 輔仁大學 === 電子工程學系 === 97 === Human pose estimation method is important for the development of behavior recognition, human-robot interaction and visual surveillance. Markerless human pose estimation method can provides non-intrusive and high-free motion capture. It has great challenges due to large range variations of motion and clothe. We propose a novel human motion capture method. The proposed method can locate human body joint position and reconstruct the human pose in 3D space from monocular images. We propose a directed probabilistic graphical model to estimate human joint positions by a devised annealing Gibbs sampling inference method. Experiments are conducted on HumanEva dataset to show the effectiveness of the proposed method. Subjects in the HumanEva have no clothe lamination and markers. The experimental data are image sequences of walking motion around a region with large ranges variation of pose. Experimental results show that the proposed method can estimate human pose from monocular images efficiently.
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