中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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
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)
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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