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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Shen, C., Lin, X., Shi, Y. (2006). Human Pose Estimation from Polluted Silhouettes Using Sub-manifold Voting Strategy. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_6
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DOI: https://doi.org/10.1007/11821045_6
Publisher Name: Springer, Berlin, Heidelberg
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