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题名: Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking
作者: Fan BJ(范保杰) ; Du YK(杜英魁) ; Cong Y(丛杨)
作者部门: 机器人学研究室
关键词: SPARSE APPEARANCE MODEL ; VISUAL TRACKING ; FEATURE-SELECTION ; K-SVD ; REPRESENTATION ; RECOGNITION ; FEATURES
刊名: ABSTRACT AND APPLIED ANALYSIS
ISSN号: 1085-3375
出版日期: 2014
页码: 1-12
收录类别: SCI
产权排序: 2
摘要: 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.
语种: 英语
WOS记录号: WOS:000338651900001
WOS标题词: Science & Technology ; Physical Sciences
类目[WOS]: Mathematics, Applied ; Mathematics
关键词[WOS]: SPARSE APPEARANCE MODEL ; VISUAL TRACKING ; FEATURE-SELECTION ; K-SVD ; REPRESENTATION ; RECOGNITION ; FEATURES
研究领域[WOS]: Mathematics
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/15120
Appears in Collections:机器人学研究室_期刊论文

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Recommended Citation:
范保杰; 杜英魁; 丛杨.Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking,ABSTRACT AND APPLIED ANALYSIS,2014,():1-12
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文件名: Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking.pdf
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