Abstract and Applied Analysis

Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

Baojie Fan, Yingkui Du, and Yang Cong

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Abstract

A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multiclassifier jointly using iterative reweighed least squares algorithm. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness.

Article information

Source
Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 189317, 12 pages.

Dates
First available in Project Euclid: 6 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412606365

Digital Object Identifier
doi:10.1155/2014/189317

Zentralblatt MATH identifier
07021901

Citation

Fan, Baojie; Du, Yingkui; Cong, Yang. Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 189317, 12 pages. doi:10.1155/2014/189317. https://projecteuclid.org/euclid.aaa/1412606365


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