title: 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.
authors.affiliation: Institute of Information Science, Academia Sinica, Taipei, Taiwan, ROC. wychang@iis.sinica.edu.tw
authors.forename: Wen-Yan
authors.initials: WY
authors.lastname: Chang
authors.forename: Chu-Song
authors.initials: CS
authors.lastname: Chen
authors.forename: Yong-Dian
authors.initials: YD
authors.lastname: Jian
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*