Clustering based ensemble correlation tracking

doi:10.1016/j.cviu.2016.05.006

	Clustering based ensemble correlation tracking
	Zhu, Guibo; Wang, Jinqiao; Lu, Hanqing
发表期刊	COMPUTER VISION AND IMAGE UNDERSTANDING
	2016-12-01
卷号	153 期号:1 页码:55-63
文章类型	Article
摘要	Correlation filter based tracking has attracted many researchers' attention in the recent years for its high efficiency and robustness. Most existing work has focused on exploiting different characteristics with correlation filter for visual tracking, e.g., circulant structure, kernel trick, effective feature representation and context information. Despite much success having been demonstrated, numerous issues remain to be addressed. Firstly, the target appearance model can not precisely represent the target in the tracking process because of the influence of scale variation. Secondly, online correlation tracking algorithms often encounter the model drift problem. In this paper, we propose a clustering based ensemble correlation tracker to deal with the above problems. Specifically, we extend the tracking correlation filter by embedding a scale factor into the kernelized matrix to handle the scale variation. Furthermore, a novel non-parametric sequential clustering method is proposed for efficiently mining the low rank structure of historical object representation through weighted cluster centers. Moreover, to alleviate the model drift, an object spatial distribution is obtained by matching the adaptive object template learned from the cluster centers. Similar to a coarse-to-fine search strategy, the spatial distribution is not only used for providing weakly supervised information, but also adopted to reduce the computational complexity in the detection procedure which can alleviate the model drift problem effectively. In this way, the proposed approach could estimate the object state accurately. Extensive experiments show the superiority of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.
关键词	Object Tracking Sequential Clustering Correlation Filter
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.cviu.2016.05.006
关键词[WOS]	VISUAL TRACKING ; OBJECT TRACKING ; BENCHMARK ; MODEL
收录类别	SCI
语种	英语
项目资助者	863 Program(2014AA015104) ; National Natural Science Foundation of China(61273034 ; 61332016)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000389566500006
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11755
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Wang, Jinqiao
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhu, Guibo,Wang, Jinqiao,Lu, Hanqing. Clustering based ensemble correlation tracking[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2016,153(1):55-63.
APA	Zhu, Guibo,Wang, Jinqiao,&Lu, Hanqing.(2016).Clustering based ensemble correlation tracking.COMPUTER VISION AND IMAGE UNDERSTANDING,153(1),55-63.
MLA	Zhu, Guibo,et al."Clustering based ensemble correlation tracking".COMPUTER VISION AND IMAGE UNDERSTANDING 153.1(2016):55-63.

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