Online structured sparse learning with labeled information for robust object tracking

Baojie Fan; Yang Cong; Yandong Tang

doi:10.1117/1.JEI.26.1.013007

20 January 2017 Online structured sparse learning with labeled information for robust object tracking

Baojie Fan, Yang Cong, Yandong Tang

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 013007 (January 2017). https://doi.org/10.1117/1.JEI.26.1.013007

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.

Citation Download Citation

Baojie Fan, Yang Cong, and Yandong Tang "Online structured sparse learning with labeled information for robust object tracking," Journal of Electronic Imaging 26(1), 013007 (20 January 2017). https://doi.org/10.1117/1.JEI.26.1.013007

Received: 25 July 2016; Accepted: 27 December 2016; Published: 20 January 2017

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