Real-time vehicles detection and traffic parameter extraction for highway surveillance

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Real-time vehicles detection and traffic parameter extraction for highway surveillance

Al-Garni, Saad Mohammad / Abdennour, Adel

Real-time freeway monitoring is becoming more and more important due to the steadily increasing traffic and the limited capacity of available highway infrastructure. Proper monitoring of highway traffic can improve the performance of such infrastructure. Highway monitoring is a challenging practical problem and a number of different approaches have been proposed in this active research area. This paper proposes a real-time vision-based monitoring system. The proposed system takes advantage of the powerful artificial neural networks (ANN) for vehicles detection and counting. The detection process uses the freeway real time images and starts by automatically extracting the image background from the successive frames. Once the background is identified, only an update is required for subsequent processing to accommodate expected environmental and light changes. The system is implemented and tested on the busiest freeway in Riyadh (King Fahd Road) and achieved a high correct detection rate in the order of 98%.