期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 1, 页码 51-63出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2393307
关键词
Contextual information; exclusive sparse learning; particle filter; tracking
类别
资金
- Advanced Digital Sciences Center, Singapore's Agency for Science, Technology and Research, under a Research Grant for the Human Sixth Sense Programme
- National Program on Key Basic Research Project (973 Program) [2012CB316304]
- National Natural Science Foundation of China [61225009]
In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple group dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L-1 tracker [1] is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.
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