聊城大学学报（自然科学版）2026，Vol.39Issue(1)：24-31,8.DOI:10.19728/j.issn1672-6634.2025020008
使用YOLOv8-OD和DeepSORT的车辆跟踪算法
Vehicle tracking algorithms using YOLOv8-OD and DeepSORT
摘要
Abstract
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.关键词
YOLOv8/DeepSORT/深度学习/车辆跟踪Key words
YOLOv8/DeepSORT/deep learning/vehicle tracking分类
交通工程引用本文复制引用
TONG Yuan,FEI Shumin..使用YOLOv8-OD和DeepSORT的车辆跟踪算法[J].聊城大学学报（自然科学版）,2026,39(1):24-31,8.基金项目
国家自然科学基金项目(61973105)资助 （61973105）
