Intelligent traffic light controlling using image processing

Electronics and communication engineering

Real-time traffic control entails calculating the amount of time each lane requires to reduce traffic congestion, as well as the timing of each red, green, and yellow light. This is accomplished by sensing the density of vehicles across four lanes. The current traffic control system encourages smart movement and efficient use of time while reducing traffic. When considering a junction of four roads with predetermined traffic signals, time will be wasted because the time allotted to each lane cannot be changed based on vehicle density. Even if there are no vehicles in a given lane, the time allotted for that lane can still be used, and the number of vehicles at other junctions may increase. Predetermined traffic lights and ageing road infrastructure cause traffic congestion, making it difficult to clear traffic in a timely manner. The primary contribution of our work is to contrast the various approaches presented for actual traffic control and to develop a new methodology to reduce the difficulties associated with traffic clearing. The same amount of time on green lights wastes resources and stresses drivers, as well as for ambulance detection. The goal of this project is to regulate traffic lights using security cameras installed at intersections using image matching techniques, Python programming, Open CV, and image processing concepts.

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