A cascade classifier-based system for identifying And counting the different kinds of vehicles using Open CV

Vehicle identification and counting are becoming more important tools for highway management. Anyhow, because cars come in a variety of sizes, identifying them remains a test that directly affects the accuracy of vehicle counts. The advancements in vehicle detection technology have significantly progressed alongside the development of autonomous driving and intelligent transportation research. This study focuses on video-based algorithms for vehicle detection and counting, utilizing OpenCV technology as the foundation for these techniques. The suggested solution use the background subtraction method to identify foreground elements in a subsequent movie. Using OpenCV, Numpy, and object detection techniques, from those moving objects, automobiles are spotted, categorised, and counted for traffic estimation. The results of an experiment reveal that the proposed counting techniques are about 84.60% percent accurate. The method is able to provide good vehicle counting accuracy with quick calculation when combined with quick motion detection and tracking techniques.