Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems
文献类型：期刊论文
| 作者 | Chang, Jianlong1 ; Wang, Lingfeng2; Meng, Gaofeng2; Xiang, Shiming2; Pan, Chunhong2
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| 刊名 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
 |
| 出版日期 | 2018-06-01 |
| 卷号 | 10期号:2页码:80-92 |
| 关键词 | Visual Occlusion Recursive Segmentation Vehicle Classification Deep Convolutional Neural Network |
| DOI | 10.1109/MITS.2018.2806619 |
| 文献子类 | Article |
| 英文摘要 | Due 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. |
| WOS关键词 | FRAMEWORK ; IMAGES ; VIDEO |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000430717200011 |
| 资助机构 | National Natural Science Foundation of China (NSFC)(91646207 ; Beijing Nature Science Foundation(4162064) ; 61403376 ; 61370039 ; 91338202) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20365]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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