Intelligent Traffic System: Yolov8 and Deepsort in Car Detection, Tracking and Counting

Abstract

Abstract: As the number of vehicles on the road increases, it becomes essential to quickly and accurately identify them to better detect traffic congestion and improve traffic management. DeepSORT (simple online and real-time tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv8 vehicle detection algorithm was proposed based on YOLOv7, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. DeepSORT introduces deep learning into the SORT algorithm by adding an appearance descriptor to reduce identity switches, making tracking more efficient. DeepSORT uses a better association metric that combines both motion and appearance descriptors. DeepSORT can be defined as the tracking algorithm that tracks objects not only based on the velocity and motion of the object but also the appearance of the object. This vehicle detection also uses the DeepSORT algorithm to help count the number of vehicles that pass in the video effectively. YOLOv8 and the DeepSORT algorithm collaborate to identify and follow vehicles creating a model, for vehicle recognition that showcases their ability to track cars efficiently. From this paper, the model Yolov8 has achieved state-of-the-art results