This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

这项研究解决了基于静态街道视频监控摄像机数据，实时获取道路交通参数质量数据的问题。现有的道路交通监控解决方案基于使用位于行车道正上方的交通摄像头，这使人们可以获取有关车辆速度和行驶方式的零碎数据。研究的目的是开发一个高质量，完整的实时数据收集系统，例如交通流强度，行驶方向和平均车速。同时，在路口和相邻路段的整个功能区域内收集数据，这些区域都落在街道视频监控摄像机角度之内。我们的解决方案基于YOLOv3神经网络架构和SORT开源跟踪器的使用。为了训练神经网络，我们标记了6000张图像并进行了增强，这使我们能够形成430万辆汽车的数据集。YOLO的基本性能通过使用附加的遮罩分支和优化锚点的形状得以改善。为了确定车速，我们使用了从原始图像到地理坐标的坐标透视转换方法。在六个交叉路口的夜间和白天对该系统进行的测试显示，车辆计数的绝对百分比准确度不少于92％。考虑到摄像机的校准，通过投影法确定车速的误差不超过1.5 km / h。YOLO的基本性能通过使用附加的遮罩分支和优化锚点的形状得以改善。为了确定车速，我们使用了从原始图像到地理坐标的坐标透视转换方法。在六个交叉路口的夜间和白天对该系统进行的测试显示，车辆计数的绝对百分比准确度不少于92％。考虑到摄像机的校准，通过投影法确定车速的误差不超过1.5 km / h。YOLO的基本性能通过使用附加的遮罩分支和优化锚点的形状得以改善。为了确定车速，我们使用了从原始图像到地理坐标的坐标透视转换方法。在六个交叉路口的夜间和白天对该系统进行的测试显示，车辆计数的绝对百分比准确度不少于92％。考虑到摄像机的校准，通过投影法确定车速的误差不超过1.5 km / h。在六个交叉路口的夜间和白天对该系统进行的测试显示，车辆计数的绝对百分比准确度不少于92％。考虑到摄像机的校准，通过投影法确定车速的误差不超过1.5 km / h。在六个交叉路口的夜间和白天对该系统进行的测试显示，车辆计数的绝对百分比准确度不少于92％。考虑到摄像机的校准，通过投影法确定车速的误差不超过1.5 km / h。