A Framework for Intelligent Traffic Control System
A Framework for Intelligent Traffic Control System
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : S. Rakesh, Nagaratna P Hegde
|DOI : 10.14445/22315381/IJETT-V70I7P210|
How to Cite?
S. Rakesh, Nagaratna P Hegde, "A Framework for Intelligent Traffic Control System" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 88-93, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P210
Recently, traffic congestion has been among the significant problems encountered by many large cities worldwide. The reasons for the traffic congestion are the hasty increase of motor vehicles and inadequate roadways to accommodate a large number of vehicles. Many researchers find the traffic density by applying edge detection (ED), moving object detection (MOD), and frame differencing techniques separately. However, the edge detection method detects the edges for static images and the MOD method finds the traffic density when vehicles are moving. Actually, in real-time, when the red signal is on a traffic junction, the vehicles are in an idle state; this situation is better to apply the ED method. When the green signal is on, vehicles immediately start moving; this situation is best suitable for applying the MOD method to find the real-time traffic density. This paper illustrates a novel technique named Edge Detection and Moving Object Detection (EDMOD) algorithm, which uses ED and MOD approaches to find the real-time area-wide density of the traffic at the traffic light junction by dividing the Region of Interest (ROI) into two regions. It uses ED in region1 and MOD in region2.
Edge Detection, Image processing, Moving Object Detection, Traffic density.
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