使用YOLOv8-OD和DeepSORT的车辆跟踪算法OA

To address the limitations of traditional multi-object tracking algorithms in terms of detection accuracy,tracking precision,and robustness,this paper proposes a novel method based on the Tracking-By-Detection paradigm for vehicle flow monitoring.The method employs the YOLOv8 object detection al-gorithm to achieve rapid localization and identification of vehicle targets,and integrates an improved deep learning-based DeepSORT multi-object tracking algorithm to ensure accurate and real-time tracking and counting of vehicles.Experimental results demonstrate that the proposed method achieves high detection accuracy when handling fast-moving vehicles,with an average precision of 94.7%.This end-to-end ap-proach exhibits good feasibility and effectiveness in batch processing applications of vehicle video data.