Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions
	Li, Ye; Li, Bo; Tian, Bin; Yao, Qingming
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2013-06-01
卷号	14 期号:2 页码:984-993
文章类型	Article
摘要	In 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.
关键词	Active Basis Model (Abm) And-or Graph (Aog) Bottom-up Inference Maximally Stable Extremal Region (Mser) Vehicle Detection
WOS标题词	Science & Technology ; Technology
关键词[WOS]	OBJECT DETECTION ; SURVEILLANCE ; CLASSIFICATION ; SYSTEMS ; SEGMENTATION ; RECOGNITION ; TRACKING
收录类别	SCI
语种	英语
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000319828800045
引用统计	被引频次：39[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3645
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 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.