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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 189317, 12 pages
Research Article

Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

1College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

Received 7 March 2014; Revised 3 June 2014; Accepted 5 June 2014; Published 24 June 2014

Academic Editor: Caihong Li

Copyright © 2014 Baojie Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.