Traffic congestion is a condition on road networks that occurs as use magnifies, and is described by slower speeds, longer trip times, and augmented conveyance queuing. The most communal example is the physical use of roads by vehicles. When traffic postulate is great enough that the interaction between vehicles slows the speed of the traffic pour, this results in some congestion. As demand approaches the competency of a road (or of the intersections along the road), extreme traffic jam sets in. When vehicles are fully stopped for epoch of time, this is conversation is known as a traffic jam or traffic snarl-up. For this, we must need an efficient traffic control system. Automatic traffic control and surveillance are important for road usage and management. Timers for each stage are the simplest way to control the traffic. Another way is to use electronic sensors in order to find out vehicles, and produce signal. Here we suggest a system that implement image processing algorithm in real time traffic light control which will control the traffic light efficiently. A web camera is placed in each stage of traffic light that will capture the still images of the road where we want to control the traffic. Then those captured images are successively matched using image matching with a reference image which is an empty road image. The traffic is governed according to percentage of matching.
Following are the steps involved
• Image acquisition
• RGB to Gray conversion
• Image enhancement using Butterworth Filter
• Edge detection using Canny,Sobel,Roberts,Prewitt and Gabor
• Image Matching
Post time: Jun-06-2017