Clustering based ensemble correlation tracking
Zhu, Guibo; Wang, Jinqiao; Lu, Hanqing
2016-12-01
发表期刊COMPUTER VISION AND IMAGE UNDERSTANDING
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
DOI10.1016/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/11755
专题模式识别国家重点实验室_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
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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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