Visual Tracking in High-Dimensional State Space by Appearance-Guided Particle Filtering

Abstract--In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a particle filtering framework is developed to realize it. Experimental results demonstrate that the proposed method is effective for high degree-of-freedom visual tracking problems, such as articulated hand tracking and lip-contour tracking.

• Type: Journal
• Year: 2008
• Where: TIP
• Authors: Wen-Yan Chang, Chu-Song Chen, Yong-Dian Jian
• Keywords: Particle Filtering Framework, Probability Propagation Model, TIP 2008, Visual Tracking