Visual tracking via adaptive multi-task feature learning with calibration and identification

Chen, Pengguang; Zhang, Xingming; Mao, Aihua; Xiong, Jiang

doi:10.1016/j.image.2016.09.009

Cited by 2 publications

(1 citation statement)

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“…The pixel intensity is robust to particle occlusion but sensitive to the shape deformation of moving target and illumination change. Chen et al [20] proposed a histogram of oriented gradients (HOGs) based sparse learning method for visual tracking. Since HOG is sensitive to occlusion, it may also cause the target drift in long‐term video sequences.…”

Section: Introductionmentioning

confidence: 99%

Robust multi‐feature visual tracking via multi‐task kernel‐based sparse learning

2017

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Feature selection and fusion is of crucial importance in multi‐feature visual tracking. This study proposes a multi‐task kernel‐based sparse learning method for multi‐feature visual tracking. The proposed sparse learning method can discriminate the reliable and unreliable features for optimal multi‐feature fusion through using a Fisher discrimination criterion‐based multi‐objective model to adaptively train the kernel weights of different features such as pixel intensity, edge and texture. To guarantee a robustness of the sparse representation method, a mixed norm is employed in the sparse leaning method to adaptively select correlated particle observations for multi‐task sparse reconstruction. Experimental results show that the proposed sparse learning method can achieve a better tracking performance than state‐of‐the‐art tracking methods do.

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Section: Introductionmentioning

confidence: 99%