Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

Journal of ICT Research and Applications

S.M.M. Kahaki¹  Md. Jan Nordin¹  Amir Hossein Ashtari¹ 

[1] Department of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia$$Department of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, MalaysiaDepartment of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia$$

关键词: : aerial image analysis;    incident detection;    Radon transform;    traffic-bottleneck detection;    traffic controlling;    vehicle detection.;   

DOI  :  10.5614/itbj.ict.2012.6.2.4

学科分类：电子、光学、磁材料

来源: Institute for Research and Community Services ITB

【 摘 要 】

One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

【 授权许可】

Unknown