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Visual tracking via robust multitask sparse prototypes

[+] Author Affiliations
Huanlong Zhang

Shanghai Jiao Tong University, School of Aeronautics and Astronautics, Shanghai 200240, China

Luoyang Institute of Science and Technology, Department of Computer and Information Engineering, Henan 471023, China

Shiqiang Hu

Shanghai Jiao Tong University, School of Aeronautics and Astronautics, Shanghai 200240, China

Junyang Yu

Central South University, Software School, Changsha 410075, China

J. Electron. Imaging. 24(2), 023025 (Apr 03, 2015). doi:10.1117/1.JEI.24.2.023025
History: Received December 16, 2014; Accepted March 11, 2015
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Abstract.  Sparse representation has been applied to an online subspace learning-based tracking problem. To handle partial occlusion effectively, some researchers introduce l1 regularization to principal component analysis (PCA) reconstruction. However, in these traditional tracking methods, the representation of each object observation is often viewed as an individual task so the inter-relationship between PCA basis vectors is ignored. We propose a new online visual tracking algorithm with multitask sparse prototypes, which combines multitask sparse learning with PCA-based subspace representation. We first extend a visual tracking algorithm with sparse prototypes in multitask learning framework to mine inter-relations between subtasks. Then, to avoid the problem that enforcing all subtasks to share the same structure may result in degraded tracking results, we impose group sparse constraints on the coefficients of PCA basis vectors and element-wise sparse constraints on the error coefficients, respectively. Finally, we show that the proposed optimization problem can be effectively solved using the accelerated proximal gradient method with the fast convergence. Experimental results compared with the state-of-the-art tracking methods demonstrate that the proposed algorithm achieves favorable performance when the object undergoes partial occlusion, motion blur, and illumination changes.

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Citation

Huanlong Zhang ; Shiqiang Hu and Junyang Yu
"Visual tracking via robust multitask sparse prototypes", J. Electron. Imaging. 24(2), 023025 (Apr 03, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.2.023025


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