Managing traffic in an urban environment has been a challenge ever since metropolitan and cosmopolitan cities started emerging. Providing a smooth traffic flow in major cities has been a challenge to city administration and road safety engineers. We are working on a research project to monitor and manage real-time traffic with the help of computer vision and machine learning.
The central idea behind the research project is to analyze various real-time factors like a number of vehicle & humans crossing a traffic signal, speed of the vehicle and other factors to predict traffic congestion, collision and various other events in the area the system is deployed.
We are using darknet framework and Yolo to detect vehicles, humans and other computer vision-related factors and using supervised learning technique to do prediction.
