Visual Vehicle Tracking Based on Conditional Random Fields
文献类型：会议论文
| 作者 | Liu, Yuqiang1,2 ; Wang, Kunfeng(王坤峰)1,2; Wang, Fei-Yue1,2
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| 出版日期 | 2014 |
| 会议名称 | 17th IEEE International Conference on Intelligent Transportation Systems |
| 会议日期 | Oct 08-11, 2014 |
| 会议地点 | Qingdao, China |
| 关键词 | Vehicle tracking conditional random fields region-level tracking |
| 卷号 | 2014 |
| 页码 | 3106-3111 |
| 通讯作者 | Wang, Kunfeng(王坤峰) |
| 中文摘要 | |
| 英文摘要 | This paper proposes an approach to moving vehicle tracking in surveillance videos based on conditional random fields (CRF). The key idea is to integrate a variety of relevant knowledge about vehicle tracking into a uniform probabilistic framework by using the CRF model. In this work, the CRF model integrates spatial and temporal contextual information of vehicle motion, and the appearance information of the vehicle. An approximate inference algorithm, loopy belief propagation, is used to recursively estimate the vehicle region from the history of observed images. Moreover, the background model is updated adaptively to cope with non-stationary background processes. Experimental results show that the proposed approach is able to accurately track moving vehicles in monocular image sequences. Besides, region-level tracking realizes precise localization of vehicles. |
| 收录类别 | EI |
| 会议录 | 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
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| 会议录出版者 | IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA |
| 学科主题 | Computer Science ; Engineering ; Transportation |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10862]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 作者单位 | 1.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yuqiang,Wang, Kunfeng,Wang, Fei-Yue. Visual Vehicle Tracking Based on Conditional Random Fields[C]. 见:17th IEEE International Conference on Intelligent Transportation Systems. Qingdao, China. Oct 08-11, 2014. |
入库方式： OAI收割
来源：自动化研究所
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