Visual Vehicle Tracking Based on Conditional Random Fields
Liu, Yuqiang1,2; Wang, Kunfeng(王坤峰)1,2; Wang, Fei-Yue1,2
2014
会议名称17th IEEE International Conference on Intelligent Transportation Systems
会议录名称2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
卷号2014
页码3106-3111
会议日期Oct 08-11, 2014
会议地点Qingdao, China
出版者IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
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.
关键词Vehicle Tracking Conditional Random Fields Region-level Tracking
学科领域Computer Science ; Engineering ; Transportation
DOI10.1109/ITSC.2014.6958189
收录类别EI
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/10862
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
通讯作者Wang, Kunfeng(王坤峰)
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]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2014:3106-3111.
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