Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions
Li, Ye; Li, Bo; Tian, Bin; Yao, Qingming
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2013-06-01
Volume14Issue:2Pages:984-993
SubtypeArticle
AbstractIn urban traffic video monitoring systems, traffic congestion is a common scene that causes vehicle occlusion and is a challenge for current vehicle detection methods. To solve the occlusion problem in congested traffic conditions, we have proposed an effective vehicle detection approach based on an AND-OR graph (AOG) in this paper. Our method includes three steps: constructing an AOG for representing vehicle objects in the congested traffic condition; training parameters in the AOG; and, finally, detecting vehicles using bottom-up inference. In AOG construction, sophisticated vehicle feature selection avoids using the easily occluded vehicle components but takes highly visible components into account. The vehicles are well represented by these selected vehicle features in the presence of a congested condition with serious vehicle occlusion. Furthermore, a hierarchical decomposition of the vehicle representation is proposed during AOG construction to further reduce the impact of vehicle occlusion. After AOG construction, all parameters in the AOG are manually learned from the training images or set and further applied to the bottom-up vehicle inference. There are two innovations of our method, i.e., the usage of the AOG in vehicle detection under congested traffic conditions and the special vehicle feature selection for vehicle representation. To fully test our method, we have done a quantitative experiment under a variety of traffic conditions, a contrast experiment, and several experiments on congested conditions. The experimental results illustrate that our method can effectively deal with various vehicle poses, vehicle shapes, and time-of-day and weather conditions. In particular, our approach performs well in congested traffic conditions with serious vehicle occlusion.
KeywordActive Basis Model (Abm) And-or Graph (Aog) Bottom-up Inference Maximally Stable Extremal Region (Mser) Vehicle Detection
WOS HeadingsScience & Technology ; Technology
WOS KeywordOBJECT DETECTION ; SURVEILLANCE ; CLASSIFICATION ; SYSTEMS ; SEGMENTATION ; RECOGNITION ; TRACKING
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000319828800045
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3645
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
AffiliationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Ye,Li, Bo,Tian, Bin,et al. Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2013,14(2):984-993.
APA Li, Ye,Li, Bo,Tian, Bin,&Yao, Qingming.(2013).Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,14(2),984-993.
MLA Li, Ye,et al."Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 14.2(2013):984-993.
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