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Monocular Tracking 3D People By Gaussian Process Spatio-Temporal Variable Model

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5 Author(s)
Junbiao Pang ; Institute of Computing Technology, Chinese Academy of Sciences, China ; Laiyun Qing ; Qingming Huang ; Shuqiang Jiang
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Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temporal variable model (GPSTVM), a novel dynamical system modeling method is proposed for learning human pose and motion priors. The GPSTVM provides a low dimensional embedding of human motion data, with a smooth density function that provides higher probability to the poses and motions close to the training data. The low dimensional latent space is optimized directly to retain the spatio-temporal structure of the high dimensional pose space. After the prior on human pose is learned, the particle filtering can be used tracking articulated human pose; particle filtering propagates over time in the embedding space, avoiding the curse of dimensionality. Experiments demonstrate that our approach tracks 3D people accurately.

Published in:

2007 IEEE International Conference on Image Processing  (Volume:5 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007