Abstract- In today’s generation of twenty first century, we haveto face several issues a well-known of that is traffic jam becoming a lot ofserious day by day. The traffic congestion can also be caused by large Redlight de-lays, etc. The delay of respective light is hard coded and it is notdependent on actual density. Therefore, for simulating and optimizing trafficcontrol to better accommodate this increasing demand is arises. this paper isabout optimization of Image processing based traffic light controller in a Cityusing raspberry pi microcontroller. The system tries to reduce possibilities oftraffic jams, caused by traffic lights, to an extent. The system is based onimage processing using python. The micro-controller used in the system isRaspberry pie. one camera is placed on respective road and capture images toanalyse traffic density. Then according to density priorities of traffic lightsignals are decided. The system contains three LEDs which are mounted on theone side of road. According to this project if traffic density is higher thetraffic signals automatically stop the signals and give green signal for thisvehicles. These techniques are in brief delineated in next section. Heretraffic density is detected using image processing, the algorithm used todetect vehicle is canny edge detection, canny edge detection is used to detectthe edges of an object and according to the no objects traffic density can bedetected.
Keywords: Traffic density, image processing, raspberry pi, led, etc.