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题名: Online discriminative dictionary learning via label information for multi task object tracking
作者: Fan BJ(范保杰); Du YK(杜英魁); Gao, Hao; Wang, Baoyun
作者部门: 机器人学研究室
会议名称: 2014 IEEE International Conference on Multimedia and Expo, ICME 2014
会议日期: July 14-18, 2014
会议地点: Chengdu, China
会议主办者: Baidu; BOCOM; et al.; NSF; NSFC; QIY
会议录: Proceedings - IEEE International Conference on Multimedia and Expo
会议录出版者: IEEE Computer Society
会议录出版地: Washington, DC
出版日期: 2014
页码: 1-6
收录类别: EI ; CPCI(ISTP)
ISSN号: 1945-7871
ISBN号: 978-1-4799-4761-4
关键词: label information ; discriminative dictionary learning ; multi task learning ; object tracking
摘要: In 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 compact 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 multi-classifier simultaneously. Combined with multi task sparse learning, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between multi task 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
产权排序: 2
WOS记录号: WOS:000360831800126
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/16816
Appears in Collections:机器人学研究室_会议论文

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