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A unified online dictionary learning framwork with label information for robust object tracking
Fan BJ(范保杰); Sun J(孙静); Cong Y(丛杨); Du YK(杜英魁)
Conference Name22nd International Conference on Pattern Recognition, ICPR 2014
Conference DateAugust 24-28, 2014
Conference PlaceStockholm, Sweden
Source PublicationProceedings; International Conference on Pattern Recognition
Publication PlacePiscataway, NJ
Indexed ByEI ; CPCI(ISTP)
EI Accession number20150100390654
WOS IDWOS:000359818002072
Contribution Rank2
KeywordLabel Information The Unified Objective Function For Online Dictionary Learning Optimal Linear Multi-classifier
AbstractIn this paper, 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 unified objective function. By minimizing the unified objective function we learn the high quality dictionary and optimal linear multi-classifier jointly. 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.
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Document Type会议论文
Corresponding AuthorFan BJ(范保杰)
Affiliation1.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China
2.State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Fan BJ,Sun J,Cong Y,et al. A unified online dictionary learning framwork with label information for robust object tracking[C]. Piscataway, NJ:IEEE,2014:2311-2316.
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