广西师范大学学报（自然科学版）2026，Vol.44Issue(2)：31-51,21.DOI:10.16088/j.issn.1001-6600.2025032504
一种基于复合框架的城市道路场景车辆轨迹提取方法
A Framework for Enhanced Vehicle Trajectory Extraction in Urban Road Scenes
摘要
Abstract
Vehicle trajectory extraction on urban roads is crucial for intelligent transportation supervision,but existing techniques suffer from low detection accuracy and broken trajectories due to identity hopping.In this paper,a composite framework that fuses improved YOLOv7-tiny detection,StrongSORT tracking and Savitzky-Golay filter optimization is proposed.The framework is capable of efficiently extracting the trajectories of different vehicle targets using urban road surveillance video data collected by traffic monitoring devices.Based on experimental evaluation,the IYSSG framework performs well in three main tasks.In vehicle detection,the improved YOLOv7-tiny algorithm ensures the detection speed,while precision,recall rate and mAP@0.5 increase by 2.5％,8.5％,and 3.7％,respectively,compared with the original YOLOv7-tiny algorithm.In terms of vehicle tracking,the StrongSORT algorithm achieves a 4.92％and 2.7％improvement in MOTA and MOTP metrics,respectively,compared with the DeepSORT algorithm.In terms of vehicle trajectory extraction and optimization,the Savitzky-Golay filtering algorithm effectively solves the problems of missing trajectory points and unsmooth trajectory due to objective factors such as video jitter and algorithmic errors,which helps the researchers to extract accurate vehicle trajectories from the traffic surveillance video for better analysis and localization of traffic problems.关键词
YOLOv7-tiny/目标检测/深度学习/多目标跟踪/轨迹提取/城市道路/车辆轨迹Key words
YOLOv7-tiny/target detection/deep learning/multi-target tracking/trajectory extraction/urban road/vehicle track分类
交通工程引用本文复制引用
田晟,冯帅涛,李嘉..一种基于复合框架的城市道路场景车辆轨迹提取方法[J].广西师范大学学报（自然科学版）,2026,44(2):31-51,21.基金项目
广东省自然科学基金(2021A1515011587) （2021A1515011587）
