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Robust Visual Tracking via Exclusive Context Modeling
2016
IEEE Transactions on Cybernetics
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
doi:10.1109/tcyb.2015.2393307
pmid:25680224
fatcat:d2qvsk2xfzactdu4mhwffcuhpu