Airborne moving vehicle detection for urban traffic surveillance
文献类型:会议论文
作者 | Lin, Renjun![]() ![]() |
出版日期 | 2008 |
会议名称 | 11th IEEE International Conference on Intelligent Transportation Systems (ITSC 2008) |
会议日期 | OCT 12-15, 2008 |
会议地点 | Beijing, PEOPLES R CHINA |
关键词 | feature extraction / filtering theory / object detection / remotely operated vehicles / road traffic / road vehicles / surveillance / airborne moving vehicle detection / filtering theory / image subtraction |
通讯作者 | Lin, Renjun |
英文摘要 | At present, moving vehicle detection on airborne platform has been an important technology for urban traffic surveillance. In such a situation, most commonly used methods (e.g. image subtraction) could hardly work well because of some additional difficulties such as slow movement of vehicles and jam. This paper proposed a new moving vehicle detection method named MVD-RD for airborne urban traffic surveillance:. First, the non-road regions tire extracted using toad detection technique. Secondly, the non-road regions with no vehicles are removed according to their size. As a result of this two-stage regions shrinkage, the detection area reduces a lot. Finally, to the reduced area, image subtraction is used to get all moving regions and then moving vehicles can be accurately filtered in a simple way. The experimental results show that, compared with traditional image subtraction, methane used in airborne moving; vehicle detection, the proposed MVD-RD method achieves much better performance in detection rate, false alarm rate, and detection speed. |
会议录 | PROCEEDINGS OF THE 11TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS
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源URL | [http://ir.ia.ac.cn/handle/173211/12818] ![]() |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
作者单位 | Univ Sci & Technol China |
推荐引用方式 GB/T 7714 | Lin, Renjun,Cao, Xianbin,Xu, Yanwu,et al. Airborne moving vehicle detection for urban traffic surveillance[C]. 见:11th IEEE International Conference on Intelligent Transportation Systems (ITSC 2008). Beijing, PEOPLES R CHINA. OCT 12-15, 2008. |
入库方式: OAI收割
来源:自动化研究所
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