Dictionary Learning for a Sparse Appearance Model in Visual Tracking

Abstract : This paper presents a novel approach to visual object tracking based on particle filtering. The tracked object is modelled by a sparse representation provided by dictionary learning. Such an approach permits to describe the target by a model of reduced dimension. The likelihood of a candidate region is built on a similarity measure between the sparse representations of a set of patches (at known positions) in the dictionary learnt from the reference template. Experimental validation is performed on various video sequences and shows the robustness of the proposed approach.
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Contributor : Sylvain Rousseau <>
Submitted on : Sunday, October 4, 2015 - 2:45:24 PM
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  • HAL Id : hal-01211263, version 1


Sylvain Rousseau, Pierre Chainais, Christelle Garnier. Dictionary Learning for a Sparse Appearance Model in Visual Tracking. ICIP, Sep 2015, Québec City, Canada. ⟨hal-01211263⟩



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