Real-Time Traffic Flow Management using OpenCV

Swift urbanization and industrialization, along with India’s large population, have pushed the country’s infrastructure to its limits. As a result, effective traffic regulation has become a critical issue in contemporary cities, where the vehicle count is growing at an unprecedented rate. The timer/countdown system that governs traffic lights in India is not only inefficient but also time-consuming in managing the traffic gridlock, eventually worsening the traffic issue, and escalating the danger of mishaps. This paper proposes a smart traffic light control system to address the problem of traffic gridlock in modern cities. The system employs computer vision and video surveillance for real-time traffic conditions. By analyzing video data from traffic cameras, the system utilizes various functions of OpenCV, including background subtraction, blob detection, and object tracking, to estimate the vehicular count in each lane and their proportional density compared to the other lanes. The time allotment module receives input on traffic density from the system and calculates the most efficient time slot to manage traffic flow. This proposed system has the potential to reduce traffic gridlock, travel times, accidents, and CO2 emissions in modern cities by taking into account real-time traffic conditions.