Traffic Analysis with Count Prediction and Speed Analysis

Traffic congestion is a persistent issue in urban areas, significantly impacting daily life. This research proposes an intelligent traffic management system utilizing computer vision techniques to address this challenge. The system employs a Faster R-CNN model to detect and count vehicles in real-time video feeds from surveillance cameras. By analyzing traffic patterns and identifying congestion hotspots, the system can provide valuable insights to traffic authorities. Additionally, frame conversion techniques can be employed to estimate vehicle speed, enabling the system to detect speeding violations and other traffic infractions. By integrating these capabilities, the proposed system aims to improve traffic flow, reduce congestion, and enhance overall road safety.