Robust Object Tracking Based on Collaborative Sparse Representation of Multifeature

doi:10.1145/3175603.3175614

CORC > 兰州理工大学 > 兰州理工大学

	Robust Object Tracking Based on Collaborative Sparse Representation of Multifeature
	Zhao, ShiLin; Li, Ming; Yang, Xinli
	2015
关键词	sparse representation multifeature fusion target tracking
DOI	10.1145/3175603.3175614
页码	27-32
英文摘要	It is inefficient when the target changes in pose variation, scale and partial occlusion. In this paper, a new target tracking algorithm with collaborative sparse representation of multifeature is proposed, which is based on the particle filter framework and sparse representation. Firstly, in the construction of the target template, both the fusion feature and HAAR features are used to describe the target, the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. Then, a two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to construct the tracking likelihood function of transient and stable appearance. Finally, the reliability of each tracker is measured by the tracking likelihood function, and the most reliable tracker is obtained by a well established particle filter framework. The templates library are incrementally updated based on the current tracking results. Experimental results show that the proposed method is robust to pose variation, scale change and partial occlusion.
会议录	PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON ROBOTICS AND ARTIFICIAL INTELLIGENCE (ICRAI 2017)
会议录出版者	ASSOC COMPUTING MACHINERY
会议录出版地	1515 BROADWAY, NEW YORK, NY 10036-9998 USA
语种	英语
资助项目	National Natural Science Foundation of China[61563030]
WOS研究方向	Computer Science ; Robotics
WOS记录号	WOS:000455828100006
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36468]
专题	兰州理工大学
通讯作者	Zhao, ShiLin
作者单位	Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Zhao, ShiLin,Li, Ming,Yang, Xinli. Robust Object Tracking Based on Collaborative Sparse Representation of Multifeature[C]. 见:.