CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Learning weighted part models for object tracking
Zhao, Chaoyang; Wang, Jinqiao; Zhu, Guibo; Wu, Yi; Lu, Hanqing
Source PublicationCOMPUTER VISION AND IMAGE UNDERSTANDING
2016-02-01
Volume143Pages:173-182
SubtypeArticle
AbstractDespite significant improvements have been made for visual tracking in recent years, tracking arbitrary object is still a challenging problem. In this paper, we present a weighted part model tracker that can efficiently handle partial occlusion and appearance change. Firstly, the object appearance is modeled by a mixture of deformable part models with a graph structure. Secondly, through modeling the temporal evolution of each part with a mixture of Gaussian distribution, we present a temporal weighted model to dynamically adjust the importance of each part by measuring the fitness to the historical temporal distributions in the tracking process. Moreover, the temporal weighted models are used to control the sample selections for the update of part models, which makes different parts update differently due to partial occlusion or drastic appearance change. Finally, the weighted part models are solved by structural learning to locate the object. Experimental results show the superiority of the proposed approach. (C) 2016 Elsevier Inc. All rights reserved.
KeywordPart Graph Model Gaussian Mixture Model Weighted Model
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.cviu.2015.10.004
WOS KeywordVISUAL TRACKING ; POSE ESTIMATION ; ONLINE
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:000369776200014
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11072
Collection模式识别国家重点实验室_图像与视频分析
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Chaoyang,Wang, Jinqiao,Zhu, Guibo,et al. Learning weighted part models for object tracking[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2016,143:173-182.
APA Zhao, Chaoyang,Wang, Jinqiao,Zhu, Guibo,Wu, Yi,&Lu, Hanqing.(2016).Learning weighted part models for object tracking.COMPUTER VISION AND IMAGE UNDERSTANDING,143,173-182.
MLA Zhao, Chaoyang,et al."Learning weighted part models for object tracking".COMPUTER VISION AND IMAGE UNDERSTANDING 143(2016):173-182.
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