title: Tracking by parts: a bayesian approach with component collaboration.
abstract: Instead of using global-appearance information for visual tracking, as adopted by many methods, we propose a tracking-by-parts (TBP) approach that uses partial appearance information for the task. The proposed method considers the collaborations between parts and derives a probability propagation framework by encoding the spatial coherence in a Bayesian formulation. To resolve this formulation, a TBP particle-filtering method is introduced. Unlike existing methods that only use the spatial-coherence relationship for particle-weight estimation, our method further applies this relationship for state prediction based on system dynamics. Thus, the part-based information can be utilized efficiently, and the tracking performance can be improved. Experimental results show that our approach outperforms the factored-likelihood and particle reweight methods, which only use spatial coherence for weight estimation.
authors.affiliation: Institute of Information Science, Academia Sinica, Taipei, Taiwan. 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: Yi-Ping
authors.initials: YP
authors.lastname: Hung