Vehicle detection grammars with partial occlusion handling for traffic surveillance
Tian, Bin1; Tang, Ming2; Wang, Fei-Yue1; Tian,Bin
AbstractTraffic 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.
KeywordComputer Vision Grammar Model Occlusion Handling Part-based Object Detection Vehicle Detection
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaTransportation
WOS SubjectTransportation Science & Technology
WOS IDWOS:000356733400006
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Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorTian,Bin
Affiliation1.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
Recommended Citation
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.
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