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

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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Junbiao Pang, Laiyun Qing, Qingming Huang, Shuqiang Jiang, Wen Gao
• Keywords: Articulated Human Pose, Dimensional Latent Space, Dimensional Pose Space, ICIP 2007, Image Processing, Low Dimensional Embedding, Particle Filtering Propagates