2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:WA-L2.3
Session:Video Object Segmentation and Tracking II
Time:Wednesday, September 19, 10:30 - 10:50
Presentation: Lecture
Title: MONOCULAR TRACKING 3D PEOPLE BY GAUSSIAN PROCESS SPATIO-TEMPORAL VARIABLE MODEL
Authors: Junbiao Pang; Institute of Computing Technology, Chinese Academy of Sciences 
 Laiyun Qing; Institute of Computing Technology, Chinese Academy of Sciences 
 Qingming Huang; Institute of Computing Technology, Chinese Academy of Sciences 
 Shuqiang Jiang; Institute of Computing Technology, Chinese Academy of Sciences 
 Wen Gao; Institute of Digital Media, Peking University 
Abstract: 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.



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