Traffic congestion is one of the leading reasons for the development of intelligent transportation systems(ITS). Traffic incidents are the second biggest cause of traffic congestions after infrastructural bottlenecks. Real-time traffic incident detection for timely clearing of roads is required to ensure smooth traffic flow. Apart from the real-time performance, scalable solutions which can monitor wide areas in a cost-effective manner are required. In this paper, robust, lean and real-time stationary foreground object detection technique to detect traffic incidents has been presented. We use block-based analysis in contrast to the conventional pixel-based analysis to lower the computational complexity of the proposed technique and achieve real-time performance. Experimental evaluations on widely used datasets demonstrate that the proposed method can achieve comparable accuracy to the existing state-of-the-art techniques. The real-time performance of the proposed system has also been demonstrated by implementing it on a low-cost embedded platform, Odroid XU-4, that still achieves a frame rate of 40 frames/second, thereby enabling real-time detection of traffic incidents.
Low complexity techniques for robust real-time traffic incident detection
2017-10-01
748672 byte
Conference paper
Electronic Resource
English
Real-Time Traffic Incident Detection with Classification Methods
Springer Verlag | 2017
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