View independent object classification by exploring scene consistency information for traffic scene surveillance
文献类型：期刊论文
| 作者 | Zhang, Zhaoxiang1; Huang, Kaiqi2; Wang, Yunhong1; Li, Min2; Kaiqi Huang ; Zhaoxiang Zhang
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2013 |
| 卷号 | 99页码:250-260 |
| 关键词 | Object classification Visual surveillance Scene division Ground plane rectification Online learning |
| 英文摘要 | We address the problem of view independent object classification. Our aim is to classify moving objects in traffic scenes surveillance videos into pedestrians, bicycles and vehicles. However, this problem is very challenging due to the following aspects. Firstly, regions of interest in videos are of low resolution and limited size due to the capacity of conventional surveillance cameras. Secondly, the intra-class variations are very large due to changes in view angles, lighting conditions and environments. Thirdly, real-time performance of algorithms is always required for real applications. Especially, perspective distortions of surveillance cameras make most 20 object features like size and speed related to view angles and not suitable for object classification. In this paper, we try to explore the hidden information of traffic scenes to deal with perspective distortions of surveillance cameras. Two solutions are given to achieve automatic object classification based on simple motion and shape features on the 20 image plane, both of which are free of large database collection and manually labeling. Abundant experiments of the two methods are conducted in videos of difference scenes and experimental results demonstrate the performance of our approaches. (C) 2012 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | RECOGNITION ; SCALE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000311129300025 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3815]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 作者单位 | 1.Beihang Univ, Beijing Key Lab Digital Media, Sch Comp Sci & Engn, Lab Intelligent Recognit & Image Proc, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Zhaoxiang,Huang, Kaiqi,Wang, Yunhong,et al. View independent object classification by exploring scene consistency information for traffic scene surveillance[J]. NEUROCOMPUTING,2013,99:250-260. |
| APA | Zhang, Zhaoxiang,Huang, Kaiqi,Wang, Yunhong,Li, Min,Kaiqi Huang,&Zhaoxiang Zhang.(2013).View independent object classification by exploring scene consistency information for traffic scene surveillance.NEUROCOMPUTING,99,250-260. |
| MLA | Zhang, Zhaoxiang,et al."View independent object classification by exploring scene consistency information for traffic scene surveillance".NEUROCOMPUTING 99(2013):250-260. |
入库方式： OAI收割
来源：自动化研究所
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