Tracking vehicles as groups in airborne videos
文献类型：期刊论文
| 作者 | Cao, Xianbin2,3; Shi, Zhengrong4; Yan, Pingkun1; Li, Xuelong1
|
| 刊名 | neurocomputing
 |
| 出版日期 | 2013 |
| 卷号 | 99页码:38-45 |
| 关键词 | Group tracking Relevance network Kalman filter Airborne platforms Multi-target tracking |
| ISSN号 | 0925-2312 |
| 产权排序 | 1 |
| 英文摘要 | airborne vehicle tracking system is receiving increasing attention due to its high mobility, low cost and large surveillance scope. however, tracking multiple vehicles simultaneously on airborne platform is a challenging problem, owing to camera vibration, which causes visible frame-to-frame jitter in the airborne videos and uncertain vehicle motion. to address these problems, a new collaborative tracking framework is proposed in this paper. the framework consists of a two-level tracking process to track vehicles as groups. the higher level builds the relevance network and divides target vehicles into different groups, where the relevance is calculated based on the status information of vehicles obtained from the lower level. the proposed group tracking takes into account the relevance between vehicles and reduces the impact of camera vibration. experimental results demonstrated that the proposed method has better performance in terms of tracking speed and tracking accuracy compared to other existing approaches based on particle filter and stationary grouping. (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] | visual tracking ; mean shift ; object |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000311129300004 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/23179]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China 2.BeiHang Univ, Sch Elect Informat Engn, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China 4.Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Xianbin,Shi, Zhengrong,Yan, Pingkun,et al. Tracking vehicles as groups in airborne videos[J]. neurocomputing,2013,99:38-45. |
| APA | Cao, Xianbin,Shi, Zhengrong,Yan, Pingkun,&Li, Xuelong.(2013).Tracking vehicles as groups in airborne videos.neurocomputing,99,38-45. |
| MLA | Cao, Xianbin,et al."Tracking vehicles as groups in airborne videos".neurocomputing 99(2013):38-45. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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