%0 Journal Article
%T Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery
%A S.M.M. Kahaki
%A Md. Jan Nordin
%A Amir Hossein Ashtari
%J ITB Journal of Information and Communication Technology
%D 2012
%I Institut Teknologi Bandung
%R 10.5614/itbj.ict.2012.6.2.4
%X 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%.
%K aerial image analysis
%K incident detection
%K Radon transform
%K traffic-bottleneck detection
%K traffic controlling
%K vehicle detection.
%U http://journal.itb.ac.id/download.php?file=C11178.pdf&id=1080&up=5