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- W2080454965 abstract "In this paper, to simultaneously address the tracker drift and occlusion problem, we propose a robust visual tracking algorithm via a patch-based adaptive appearance model driven by local background estimation. Inspired by human visual mechanisms (i.e., context-awareness and attentional selection), an object is represented with a patch-based appearance model, in which each patch outputs a confidence map during the tracking. Then, these confidence maps are combined via a robust estimator to finally get more robust and accurate tracking results. Moreover, we present a local spatial co-occurrence based background modeling approach to automatically estimate the local context background model of an interested object captured from a single camera, which may be stationary or moving. Finally, we utilize local background estimation to provide supervision to an analysis of possible occlusions and the adaption of patch-based appearance model of an object. Qualitative and quantitative experimental results on challenging videos demonstrate the robustness of the proposed method." @default.
- W2080454965 created "2016-06-24" @default.
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- W2080454965 date "2014-01-01" @default.
- W2080454965 modified "2023-09-27" @default.
- W2080454965 title "Robust tracking via patch-based appearance model and local background estimation" @default.
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- W2080454965 doi "https://doi.org/10.1016/j.neucom.2013.06.044" @default.
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