Image processing based tracking and counting vehicles

Image processing-based tracking and counting of vehicles involves utilizing algorithms to analyze video footage or images captured by cameras. The process typically begins with object detection, where vehicles are identified and localized within each frame. Techniques like background subtraction, edge detection, and machine learning models are often employed to improve accuracy.

Once vehicles are detected, tracking algorithms, such as Kalman filters or optical flow methods, are used to follow their movement across frames. This allows for real-time monitoring and counting, even in dynamic environments with varying lighting and weather conditions. Data collected can be used for traffic analysis, urban planning, and enhancing road safety measures. Overall, image processing plays a crucial role in automating vehicle management and providing insights for infrastructure improvements.