Tracking by parts: a bayesian approach with component collaboration.
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.
Chang
Wen-Yan
WY
Institute of Information Science, Academia Sinica, Taipei, Taiwan. wychang@iis.sinica.edu.tw
Chen
Chu-Song
CS
Hung
Yi-Ping
YP