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Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems
Chang, Jianlong1; Wang, Lingfeng2; Meng, Gaofeng2; Xiang, Shiming2; Pan, Chunhong2
Source PublicationIEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
2018-06-01
Volume10Issue:2Pages:80-92
SubtypeArticle
AbstractDue to the factors such as visual occlusion, illumination change and pose variation, it is a challenging task to develop effective and efficient models for vehicle detection and classification in surveillance videos. Although plenty of existing related models have been proposed, many issues still need to be resolved. Typically, vehicle detection and classification methods should be vulnerable in complex environments. Moreover, in spite of many thoughtful attempts on adaptive appearance models to solve the occlusion problem, the corresponding approaches often suffer from high computational costs. This paper aims to address the above mentioned issues. By analyzing closures and convex hulls of vehicles, we propose a simple but effective recursive algorithm to segment vehicles involved in multiple-vehicle occlusions. Specifically, a deep convolutional neural network (CNN) model is constructed to capture high level features of images for classifying vehicles. Furthermore, a new pre-training strategy based on the sparse coding and auto-encoder is developed to pre-train CNNs. After pre-training, the proposed deep model yields a high performance with a limited labeled training samples.
KeywordVisual Occlusion Recursive Segmentation Vehicle Classification Deep Convolutional Neural Network
WOS HeadingsScience & Technology ; Technology
DOI10.1109/MITS.2018.2806619
WOS KeywordFRAMEWORK ; IMAGES ; VIDEO
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(91646207 ; Beijing Nature Science Foundation(4162064) ; 61403376 ; 61370039 ; 91338202)
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000430717200011
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20365
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Dept Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chang, Jianlong,Wang, Lingfeng,Meng, Gaofeng,et al. Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,2018,10(2):80-92.
APA Chang, Jianlong,Wang, Lingfeng,Meng, Gaofeng,Xiang, Shiming,&Pan, Chunhong.(2018).Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems.IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,10(2),80-92.
MLA Chang, Jianlong,et al."Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems".IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 10.2(2018):80-92.
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