Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems | |
Chang, Jianlong1; Wang, Lingfeng2; Meng, Gaofeng2; Xiang, Shiming2; Pan, Chunhong2 | |
发表期刊 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE |
2018-06-01 | |
卷号 | 10期号:2页码:80-92 |
文章类型 | Article |
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. |
关键词 | Visual Occlusion Recursive Segmentation Vehicle Classification Deep Convolutional Neural Network |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/MITS.2018.2806619 |
关键词[WOS] | FRAMEWORK ; IMAGES ; VIDEO |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China (NSFC)(91646207 ; Beijing Nature Science Foundation(4162064) ; 61403376 ; 61370039 ; 91338202) |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000430717200011 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20365 |
专题 | 模式识别国家重点实验室_先进时空数据分析与学习 |
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. |
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 | ||
Vision-Based Occlusi(789KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论