Human pose estimation from polluted silhouettes using sub-manifold voting strategy

Authors:
Chunfeng Shen;Xueyin Lin;Yuanchun Shi
Affiliations:
Key Lab of Pervasive Computing(MOE), Dept. of Computer Science & Technology, Tsinghua University, Beijing, P.R. China;Key Lab of Pervasive Computing(MOE), Dept. of Computer Science & Technology, Tsinghua University, Beijing, P.R. China;Key Lab of Pervasive Computing(MOE), Dept. of Computer Science & Technology, Tsinghua University, Beijing, P.R. China
Venue:
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Year:
2006

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Human pose estimation from corrupted silhouettes using a sub-manifold voting strategy in latent variable space

Pattern Recognition Letters

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Abstract

In this paper, we introduce a framework of human pose estimation from polluted silhouettes due to occlusions or shadows. Since the body pose (and configuration) can be estimated by partial components of the silhouette, a robust statistical method is applied to extract useful information from these components. In this method a Gaussian Process model is used to create each sub-manifold corresponding to the component of input data in advance. A sub-manifold voting strategy is then applied to infer the pose structure based on these sub-manifolds. Experiments show that our approach has a great ability to estimate human poses from polluted silhouettes with small computational burden.