基于稀疏帧检测的交通目标跟踪

为了获取高速公路交通视频中目标车辆的行驶轨迹,提出一种基于视频的多目标车辆跟踪及实时轨迹分布算法,为交通管理系统和交通决策提供目标车辆交通信息.首先,使用YOLOv4算法检测目标车辆位置及置信度.其次,在不同场景条件下,使用提出的基于稀疏帧检测的跟踪方法,结合KCF跟踪算法,将车辆数据进行关联获取完整轨迹.最后,用车辆分布图和交通场景俯视图显示轨迹,便于交通管理与分析.实验结果表明,提出的跟踪方法在车辆跟踪中有较高的跟踪正确率,同时基于稀疏帧检测的跟踪方法处理速度也较快,实时轨迹分布正确反映了真实场景的车道信息以及目标车辆运动信息.

Traffic Object Tracking Based on Sparse Frame Detection

A video-based multi-object vehicle tracking and real-time trajectory distribution algorithm is proposed to display the driving trajectories of vehicles in a highway traffic video, which can provide useful traffic information for traffic management and decision-making. Firstly, the YOLOv4 algorithm is used to detect vehicle objects. Secondly, in different traffic scenarios, the vehicle data is correlated to yield a complete trajectory by using the proposed tracking method based on sparse frame detection in combination with KCF tracking algorithm. Finally, the vehicle trajectory is displayed with the vehicle distribution map and the top view of traffic scenes, which is convenient for traffic management and analysis. Experimental results show that the proposed vehicle tracking method has an excellent tracking accuracy and a fast processing speed. The real-time trajectory distribution correctly reflects the lane information of real scenes and movement information of the object vehicles, which has a great application value.