Multi-view tracking of articulated human motion in silhouette and pose manifolds

This paper presents a multi-view articulated human motion tracking framework using particle filter with manifold learning through Gaussian process latent variable model. The dimensionality of the input image observation and joint angles are reduced using Gaussian process models to improve the tracking efficiency. The forward and backward mappings between the two low dimensional spaces are then obtained using relevance vector machine and Batesian mixture of experts (BME). Improved sampling schemes and auto-initialization are obtained using BME. Without using a 3D body model, effective likelihood evaluation is obtained through RVM using images from multiple views. Tracking results obtained using real videos with complex dance movement show the efficacy of the proposed approach.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Feng Guo, Gang Qian
• Keywords: Gaussian Process, Gaussian Process Models, ICASSP 2009, Motion Tracking Framework, Signal Processing