Knowledge Commons of Institute of Automation，CAS
| Visual Tracking Based on Dynamic Coupled Conditional Random Field Model | |
Liu, Yuqiang1,2 ; Wang, Kunfeng1; Shen, Dayong3; Wang, Kunfeng(王坤峰)
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| 发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 2016-03-01 | |
| 卷号 | 17期号:3页码:822-833 |
| 文章类型 | Article |
| 摘要 | This 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. |
| 关键词 | Coupled Conditional Random Field Dynamic Models Visual Tracking Region-level Tracking Spatiotemporal Context |
| WOS标题词 | Science & Technology ; Technology |
| 学科领域 | Civil Engineering |
| DOI | 10.1109/TITS.2015.2488287 |
| 关键词[WOS] | VEHICLE DETECTION ; OBJECT TRACKING ; SEGMENTATION ; VIDEO ; INFORMATION ; INTEGRATION ; OCCLUSIONS ; BEHAVIOR ; FLOW |
| URL | 查看原文 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | National Natural Science Foundation of China(61304200) ; MIIT Project of Internet of Things Development Fund(1F15E02) |
| WOS研究方向 | Engineering ; Transportation |
| WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
| WOS记录号 | WOS:000371982600019 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/10860 |
| 专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
| 通讯作者 | Wang, Kunfeng(王坤峰) |
| 作者单位 | 1.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 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 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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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| 2016Visual Tracking （1757KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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