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- W2003184090 abstract "In this paper, we propose a novel and robust object tracking algorithm based on sparse representation. Object tracking is formulated as a object recognition problem rather than a traditional search problem. All target candidates are considered as training samples and the target template is represented as a linear combination of all training samples. The combination coefficients are obtained by solving for the minimum l1-norm solution. The final tracking result is the target candidate associated with the non-zero coefficient. Experimental results on two challenging test sequences show that the proposed method is more effective than the widely used mean shift tracker." @default.
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- W2003184090 date "2010-07-11" @default.
- W2003184090 modified "2023-09-23" @default.
- W2003184090 title "Robust object tracking based on sparse representation" @default.
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- W2003184090 doi "https://doi.org/10.1117/12.863437" @default.
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