| Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment | |
| Cao, Xianbin1; Lin, Renjun2; Yan, Pingkun3; Li, Xuelong3 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2012-03-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| ISSN | 1051-8215 |
| 卷号 | 22期号:3页码:366-378 |
| 产权排序 | 3 |
| 摘要 | One of the primary goals of the airborne vehicle detection system is to reduce the risks of incident collisions and to relieve traffic jam caused by the increasing number of vehicles. Different from the stationary systems, which are usually fixed on buildings, the airborne systems in unmanned aircrafts or satellites take the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. The direct application of the traditional image processing techniques to the problem may result in low detection rate or cannot meet the requirements of real-time applications. To address these problems, a new and efficient method composed by two stages, attention focus extraction and vehicle classification is proposed in this paper. Our work makes two key contributions. The first is the introduction of a new attention focus extraction algorithm, which can quickly detect the candidate vehicle regions to make the algorithm focus on much smaller regions for faster computation. The second contribution is a simple and efficient classification process, which is built using the AdaBoost learning algorithm. The classification process, which is a hierarchical structure, is designed to obtain a lower false alarm rate by looking for vehicles in the candidate regions. Experimental results demonstrate that, compared with other representative algorithms, our method can obtain better performance in terms of higher detection rate and lower false positive rate, while meeting the needs of real-time application. |
| 文章类型 | Article |
| 关键词 | Airborne Vehicles Detection System (Avds) Attention Focus Attention Shifting Cascade Classifier Extent Tracing Statistical Learning |
| 学科领域 | Engineering |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCSVT.2011.2163443 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | SURVEILLANCE ; INFORMATION |
| 语种 | 英语 |
| WOS研究方向 | Engineering |
| WOS类目 | Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000301235700004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/20247 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Beihang Univ, Sch Elect & Informat Engn, Beijing 100083, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Xianbin,Lin, Renjun,Yan, Pingkun,et al. Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2012,22(3):366-378. |
| APA | Cao, Xianbin,Lin, Renjun,Yan, Pingkun,&Li, Xuelong.(2012).Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,22(3),366-378. |
| MLA | Cao, Xianbin,et al."Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 22.3(2012):366-378. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Visual Attention Acc（9873KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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