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Journal of Electronic Imaging

Visual tracking via robust multitask sparse prototypes
Author(s): Huanlong Zhang; Shiqiang Hu; Junyang Yu
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Paper 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.

Paper Details

Date Published: 3 April 2015
PDF: 12 pages
J. Electron. Imaging. 24(2) 023025 doi: 10.1117/1.JEI.24.2.023025
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Huanlong Zhang, Shanghai Jiao Tong Univ. (China)
Luoyang Institute of Science & Technology (China)
Shiqiang Hu, Shanghai Jiao Tong Univ. (China)
Junyang Yu, Central South Univ. (China)


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