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• W2074455057 abstract "Sparse representation has been popular in visual tracking recently for its robustness and accuracy. However, for most conventional sparse coding based trackers, the target candidates are considered independently between consecutive frames. This paper shows that the temporal correlation of these frames can be exploited to improve the performance of tracking and makes the tracker more robust to noise. Furthermore, to improve the tracking speed, we revisit a more efficient method for ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> norm problem, marginal regression, which can solve the sparse coding problem more efficiently. Consequently we can realize real-time tracking based on the temporal smooth sparse representation. Extensive experiments have been done to demonstrate the effectiveness and efficiency of our method." @default.
• W2074455057 created "2016-06-24" @default.
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• W2074455057 date "2015-09-01" @default.
• W2074455057 modified "2023-09-22" @default.
• W2074455057 title "Visual Tracking via Temporally Smooth Sparse Coding" @default.
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• W2074455057 doi "https://doi.org/10.1109/lsp.2014.2365363" @default.
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