Title :
Tracking by Parts: A Bayesian Approach With Component Collaboration
Author :
Chang, Wen-Yan ; Chen, Chu-Song ; Hung, Yi-Ping
Author_Institution :
Inst. of Inf. Sci., Acad. Sinica, Taipei
fDate :
4/1/2009 12:00:00 AM
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.
Keywords :
Bayes methods; image processing; particle filtering (numerical methods); tracking; Bayesian approach; Bayesian formulation; component collaboration; factored-likelihood; global-appearance information; part-based information; particle reweight method; particle-filtering method; particle-weight estimation; probability propagation framework; spatial coherence; state prediction; system dynamics; tracking-by-parts; visual tracking performance; Component collaboration; contrast histogram; particle filtering; tracking by parts (TBP); visual tracking;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Conference_Location :
12/16/2008 12:00:00 AM
DOI :
10.1109/TSMCB.2008.2005417