【摘要】 车辆目标跟踪是实现车联网不可或缺的一环,旨在获取车辆的动态信息,以提高交通运行效率。其核心是对大量监控探头采集的视频图像进行分析处理,实现车辆的实时检测与跟踪。为了进一步提高目标检测效率,降低硬件成本,文中提出了基于二帧差分法的前景检测方法,以及基于质心法的车辆轮廓检测与跟踪方法。基于OpenCV3.4.1和VS2017进行验证实验和仿真测试,结果表明,该算法对车辆跟踪的精确率达到89.1%,平均处理耗时42.63 ms,具有较好的实时性和鲁棒性,可在车联网嵌入式设备上进行部署和应用。更多还原

【Abstract】 Vehicle target tracking is an indispensable part of the realization of the Internet of Vehicles, which aims to obtain vehicle dynamic information to improve the efficiency of traffic operation.Its core is to analyze and process the video images collected by a large number of monitoring probes to realize real-time detection and tracking of vehicles.In order to further improve the efficiency of target detection and reduce hardware costs, this paper proposes a foreground detection method based on the two-frame difference method, and a vehicle contour detection and tracking method based on the centroid method.Based on OpenCV3.4.1 and VS2017,the algorithm verification and simulation test are carried out.The results show that the accuracy of the algorithm for vehicle tracking reaches 92.3%,and the average processing time is 42.63 ms.It can be deployed and applied on embedded devices in the Internet of Vehicles.更多还原