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Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion

文献类型：期刊论文

作者Wang, Lingfeng1; Yan, Hongping2; Lv, Ke2; Pan, Chunhong1
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2014-07-01
卷号24期号:7页码:1132-1141
关键词Kernel sparse representation (KSR) multikernel fusion visual tracking
英文摘要It 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.
WOS标题词Science & Technology ; Technology
类目[WOS]Engineering, Electrical & Electronic
研究领域[WOS]Engineering
关键词[WOS]OBJECT TRACKING ; ONLINE SELECTION ; MEAN-SHIFT ; FEATURES ; MODEL
收录类别SCI
语种英语
WOS记录号WOS:000340102500006
源URL[http://ir.ia.ac.cn/handle/173211/3705]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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