Vehicle detection grammars with partial occlusion handling for traffic surveillance

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 grammars with partial occlusion handling for traffic surveillance
	Tian, Bin1 ; Tang, Ming2 ; Wang, Fei-Yue1 ; Tian,Bin
发表期刊	TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
	2015-07-01
卷号	56 期号:0 页码:80-93
文章类型	Article
摘要	Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehicle detection method by designing vehicle detection grammars and handling partial occlusion. The grammar model is implemented by novel detection grammars, including structure, deformation and pairwise SVM grammars. First, the vehicle is divided into its constitute parts, called semantic parts, which can represent the vehicle effectively. To increase the robustness of part detection, the semantic parts are represented by their detection score maps. The semantic parts are further divided into sub-parts automatically. The two-layer division of the vehicle is modeled into a grammar model. Then, the grammar model is trained by a designed training procedure to get ideal grammar parameters, including appearance models and grammar productions. After that, vehicle detection is executed by a designed detection procedure with respect to the grammar model. Finally, the issue of vehicle occlusion is handled by designing and training specific grammars. The strategy adopted by our method is first to divide the vehicle into the semantic parts and sub-parts, then to train the grammar productions for semantic parts and sub-parts by introducing novel pairwise SVM grammars and finally to detect the vehicle by applying the trained grammars. Experiments in practical urban scenarios are carried out for complex traffic surveillance. It can be shown that our method adapts to partial occlusion and various challenging cases. (C) 2015 Elsevier Ltd. All rights reserved.
关键词	Computer Vision Grammar Model Occlusion Handling Part-based Object Detection Vehicle Detection
WOS标题词	Science & Technology ; Technology
关键词[WOS]	TRACKING ; SEGMENTATION ; FEATURES ; CAMERA ; SYSTEM
收录类别	SCI
语种	英语
WOS研究方向	Transportation
WOS类目	Transportation Science & Technology
WOS记录号	WOS:000356733400006
引用统计	被引频次：18[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/173211/7906
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
通讯作者	Tian,Bin
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 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	Tian, Bin,Tang, Ming,Wang, Fei-Yue,et al. Vehicle detection grammars with partial occlusion handling for traffic surveillance[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2015,56(0):80-93.
APA	Tian, Bin,Tang, Ming,Wang, Fei-Yue,&Tian,Bin.(2015).Vehicle detection grammars with partial occlusion handling for traffic surveillance.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,56(0),80-93.
MLA	Tian, Bin,et al."Vehicle detection grammars with partial occlusion handling for traffic surveillance".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 56.0(2015):80-93.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Vehicle Detection Gr（2568KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览