计算机工程与应用2023，Vol.59Issue(24)：88-97,10.DOI:10.3778/j.issn.1002-8331.2307-0017
融合时空特征的隧道场景跨相机车辆实时跟踪方法
Real-Time Cross-Camera Vehicle Tracking Method for Tunnel Scenes by Fusing Spatiotemporal Features
摘要
Abstract
Cross-camera vehicle tracking is of great significance for realizing intelligent transportation.In the tunnel scene,the existing target re-identification scheme is difficult to meet the requirements of vehicle tracking accuracy and real-time in practical applications due to the influence of factors such as low environmental illumination and similar char-acteristics of the same type of vehicles.A cross-camera multi-target tracking method is proposed considering the vehicle type and spatiotemporal characteristics of vehicles in tunnel traffic scenarios.Firstly,the normalized attention module(NAM)is added to the YOLOv7 target detection model to make the model more focused on the region of interest,and combining with the camera calibration method,the vehicle position coordinates are obtained in real space.Secondly,the target position prediction is combined with vehicle velocity based on Kalman filtering,secondary correlation strategy(BYTE)is applied to complete single camera vehicle tracking,and interval frame method is used to improve the tracking speed.Finally,the cross-camera target matching cost matrix based on vehicle type and spatiotemporal characteristics is proposed,and the Hungarian algorithm is used to complete vehicle target matching,so as to realize cross-camera vehicle target tracking and generate the vehicle target spatiotemporal map of the tunnel scene.The experimental results on the cross-camera vehicle target tracking dataset constructed show that the tracking accuracy reaches 82.1%,the overall speed of detection and tracking reaches 115 frames per second,the accuracy of cross-camera target matching reaches 94.9%,and the tracking speed and accuracy are better than other methods.关键词
跨相机目标跟踪/隧道场景/时空特征/注意力机制/二次关联策略Key words
cross-camera target tracking/tunnel scene/spatiotemporal feature/attention mechanism/secondary correlation strategy分类
信息技术与安全科学引用本文复制引用
苟铃滔,宋焕生,张朝阳,文雅,刘莅辰,孙士杰..融合时空特征的隧道场景跨相机车辆实时跟踪方法[J].计算机工程与应用,2023,59(24):88-97,10.基金项目
国家自然科学基金(62006026,62072053,U21B2041). （62006026,62072053,U21B2041）
