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Amir H. Ashtari
Department of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia
Computer Science & IT

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Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery Sayed M.M. Kahaki; Md. Jan Nordin; Amir H. Ashtari
Journal of ICT Research and Applications Vol. 6 No. 2 (2012)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2012.6.2.4

Abstract

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%.
Co-Authors Md. Jan Nordin Sayed M.M. Kahaki