Visual Vehicle Tracking Based on Conditional Random Fields
Liu, Yuqiang1,2; Wang, Kunfeng(王坤峰)1,2; Wang, Fei-Yue1,2
2014
Conference Name17th IEEE International Conference on Intelligent Transportation Systems
Source Publication2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Volume2014
Pages3106-3111
Conference DateOct 08-11, 2014
Conference PlaceQingdao, China
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract
; 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.
KeywordVehicle Tracking Conditional Random Fields Region-level Tracking
Subject AreaComputer Science ; Engineering ; Transportation
DOI10.1109/ITSC.2014.6958189
Indexed ByEI
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10862
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.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
Recommended Citation
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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