Traffic Analysis with Lane-Level Vehicle Speed Estimation

Thanks to advances in computer power, traffic surveillance video processing is using deep learning algorithms more and more. Estimating a vehicle's speed is essential for managing traffic, lowering accident rates, and enhancing road design. Although they bring complexity issues, vision-based systems for speed estimate have advantages including accurate vehicle recognition and cheaper costs. A solution was put out in the 2018 NVIDIA AI City Challenge, which combined deep learning models with conventional computer vision techniques. The approach measures transit time between frames to estimate speed using Python and OpenCV image processing techniques including feature extraction and vehicle tracking. The significance of speed estimate for traffic safety and management is emphasized by this study.