CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion
Wang, Lingfeng1; Yan, Hongping2; Lv, Ke2; Pan, Chunhong1
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
2014-07-01
Volume24Issue:7Pages:1132-1141
SubtypeArticle
AbstractIt remains a challenging task to track an object robustly due to factors such as pose variation, illumination change, occlusion, and background clutter. In the past decades, a number of researchers have been attracted to tackling these difficulties, and they proposed many effective methods. Among them, sparse representation-based tracking method is a promising. While much success has been demonstrated, there are several issues that still need to be addressed. First, the introduction to trivial occlusion templates brings a high computational cost of this method. Second, the utilization of raw template object representation makes this method difficult to adopt sophisticated object features. To solve these problems, we consider the sparse representation problem in a kernel space and propose a kernel sparse representation (KSR)-based tracking algorithm. Under the kernel representation, it is not necessary to introduce trivial occlusion templates in order to reduce the computational cost. Furthermore, multikernel fusion allows our method to use multiple sophisticated object features, such as spatial color histogram and spatial gradient-orientation histogram, and let these features complement each other during the tracking process. Comparative experiments on challenging scenes demonstrate that our KSR-based tracking algorithm outperforms the state-of-the-art approaches in tracking accuracy.
KeywordKernel Sparse Representation (Ksr) Multikernel Fusion Visual Tracking
WOS HeadingsScience & Technology ; Technology
WOS KeywordOBJECT TRACKING ; ONLINE SELECTION ; MEAN-SHIFT ; FEATURES ; MODEL
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000340102500006
Citation statistics
Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3705
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Chinese Acad Sci, Inst Automat, Dept Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.China Univ Geosci, Coll Informat & Engn, Beijing 100083, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Lingfeng,Yan, Hongping,Lv, Ke,et al. Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2014,24(7):1132-1141.
APA Wang, Lingfeng,Yan, Hongping,Lv, Ke,&Pan, Chunhong.(2014).Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,24(7),1132-1141.
MLA Wang, Lingfeng,et al."Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 24.7(2014):1132-1141.
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