Visual Tracking Based on Dynamic Coupled Conditional Random Field Model
Liu, Yuqiang1,2; Wang, Kunfeng1; Shen, Dayong3; Wang, Kunfeng(王坤峰)
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2016-03-01
Volume17Issue:3Pages:822-833
SubtypeArticle
AbstractThis paper proposes a novel approach to visual tracking of moving objects based on the dynamic coupled conditional random field (DcCRF) model. The principal idea is to integrate a variety of relevant knowledge about object tracking into a unified dynamic probabilistic framework, which is called the DcCRF model in this paper. Under this framework, the proposed approach integrates spatiotemporal contextual information of motion and appearance, as well as the compatibility between the foreground label and object label. An approximate inference algorithm, i.e., loopy belief propagation, is adopted to conduct the inference. Meanwhile, the background model is adaptively updated to deal with gradual background changes. Experimental results show that the proposed approach can accurately track moving objects (with or without occlusions) in monocular video sequences and outperforms some state-of-the-art methods in tracking and segmentation accuracy.
KeywordCoupled Conditional Random Field Dynamic Models Visual Tracking Region-level Tracking Spatiotemporal Context
WOS HeadingsScience & Technology ; Technology
Subject AreaCivil Engineering
DOI10.1109/TITS.2015.2488287
WOS KeywordVEHICLE DETECTION ; OBJECT TRACKING ; SEGMENTATION ; VIDEO ; INFORMATION ; INTEGRATION ; OCCLUSIONS ; BEHAVIOR ; FLOW
URL查看原文
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61304200) ; MIIT Project of Internet of Things Development Fund(1F15E02)
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000371982600019
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10860
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorWang, Kunfeng(王坤峰)
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
3.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst, Changsha 410073, Hunan, Peoples R China
Recommended Citation
GB/T 7714
Liu, Yuqiang,Wang, Kunfeng,Shen, Dayong,et al. Visual Tracking Based on Dynamic Coupled Conditional Random Field Model[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2016,17(3):822-833.
APA Liu, Yuqiang,Wang, Kunfeng,Shen, Dayong,&Wang, Kunfeng.(2016).Visual Tracking Based on Dynamic Coupled Conditional Random Field Model.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,17(3),822-833.
MLA Liu, Yuqiang,et al."Visual Tracking Based on Dynamic Coupled Conditional Random Field Model".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 17.3(2016):822-833.
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