CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Clustering based ensemble correlation tracking
Zhu, Guibo; Wang, Jinqiao; Lu, Hanqing
Source PublicationCOMPUTER VISION AND IMAGE UNDERSTANDING
2016-12-01
Volume153Issue:1Pages:55-63
SubtypeArticle
AbstractCorrelation 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.
KeywordObject Tracking Sequential Clustering Correlation Filter
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.cviu.2016.05.006
WOS KeywordVISUAL TRACKING ; OBJECT TRACKING ; BENCHMARK ; MODEL
Indexed BySCI
Language英语
Funding Organization863 Program(2014AA015104) ; National Natural Science Foundation of China(61273034 ; 61332016)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000389566500006
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11755
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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