Journal of Computer Science

Highway Traffic Incident Detection using High-Resolution Aerial Remote Sensing Imagery

Seyed Mostafa Mousavi Kahaki and Md Jan Nordin

DOI : 10.3844/jcssp.2011.949.953

Journal of Computer Science

Volume 7, Issue 6

Pages 949-953


Problem statement: As vehicle population increases, Intelligent Transportation Systems (ITS) become more significant and mandatory in today’s overpopulated world. Vital problems in transportation such as mobility and safety of transportation are considered more, especially in metropolitans and highways. The main road traffic monitoring aims are: the acquisition and analysis of traffic figures, such as number of vehicles, incident detection and automatic driver warning systems are developed mainly for localization and safety purposes. Approach: The objective of this investigation was to propose a strategy for road extraction and incident detection using aerial images. Real time extraction and localization of roadways in an satellite image is an emerging research field which can applied to vision-based traffic controlling and unmanned air vehicles navigation. Results: The results of the proposed incident detection algorithm show that it has good detection performance, the maximum angle of vehicles applied for incidet detection is 45° and the performance for learning system in order to vehicle detection is 86%. This performance achived in testing algorithm on 45 highway aerial images. Conclusion: In order to consider with the high complexity of this kind of imagery, we integrate knowledge about roadways using formulated scale-dependent models. The intensity images are used for the extraction of road from satellite images. Threshold techniques, neural network and Radon transform are used for the road extraction, vehicle detection and incident detection. Results indicated that in most aerial images the incident can be detect by applying the angle algorithm.


© 2011 Seyed Mostafa Mousavi Kahaki and Md Jan Nordin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.