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Journal of Electronic Imaging

Online structured sparse learning with labeled information for robust object tracking
Author(s): Baojie Fan; Yang Cong; Yandong Tang
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Paper Abstract

We formulate object tracking under the particle filter framework as a collaborative tracking problem. The priori information from training data is exploited effectively to online learn a discriminative and reconstructive dictionary, simultaneously without losing structural information. Specifically, the class label and the semantic structure information are incorporated into the dictionary learning process as the classification error term and ideal coding regularization term, respectively. Combined with the traditional reconstruction error, a unified dictionary learning framework for robust object tracking is constructed. By minimizing the unified objective function with different mixed norm constraints on sparse coefficients, two robust optimizing methods are developed to learn the high-quality dictionary and optimal classifier simultaneously. The best candidate is selected by minimizing the reconstructive error and classification error jointly. As the tracking continues, the proposed algorithms alternate between the robust sparse coding and the dictionary updating. The proposed trackers are empirically compared with 14 state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons demonstrate that the proposed algorithms perform well in terms of accuracy and robustness.

Paper Details

Date Published: 20 January 2017
PDF: 16 pages
J. Electron. Imag. 26(1) 013007 doi: 10.1117/1.JEI.26.1.013007
Published in: Journal of Electronic Imaging Volume 26, Issue 1
Show Author Affiliations
Baojie Fan, Nanjing University of Posts and Telecommunications (China)
Yang Cong, Chinese Academy of Sciences (China)
Yandong Tang, Chinese Academy of Sciences (China)

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