Authors : Teena Varma, Cline Colaco, Mandar Bagwe and Siddharth Bose
Abstract: Traffic jams have become one of the biggest problems any metropolitan city faces in today’s time. This paper suggests implementing a smart traffic detector using OpenCV. The density of vehicles on the road keeps increasing to a higher amount these days. In traffic signal, people waste much time particularly during the peak hours of the day. In order to solve this problem of high traffic pressure, it is indispensable to solve traffic congestion. The frustration that is faced by people during traffic jams could also lead to mishaps such as accidents. Thus an idea of monitoring the traffic congestion using real-time image processing techniques and via. Central Neural Networks, through this software has been proposed. The theme is to determine the traffic density on each side of the road by calculating the number of vehicles at the traffic signal zone. In this, an input image of traffic surveillance is shown to our trained machine which declares whether there is traffic or not by judging via. the number of vehicles seen. After the image acquisition, the image undergoes various image pre-processing, image enhancement and edge detection techniques. This project has been customized to be used in the future to control the traffic signals as well as monitor violators and avoid inconvenience and accidents as much as possible.
Teena Varma, Cline Colaco, Mandar Bagwe and Siddharth Bose, 2020. Traffic Detection Using OpenCV. International Journal of Signal System Control and Engineering Application, 13: 18-22.