Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.

实时公路交通监控系统在道路交通管理、规划和预防频繁的交通拥堵、违反交通规则和致命的道路事故中发挥着至关重要的作用。这些系统完全依赖于从与时间相关的车辆轨迹估计的在线交通流量信息。车辆轨迹是从处理路边摄像机图像获得的车辆检测和跟踪数据中提取的。包括 Yolo、SSD、EfficientNet 在内的通用目标检测器已被广泛用于实时目标检测任务，但原则上，Yolo 是首选，因为它提供高每秒帧数 (FPS) 性能和强大的目标定位功能。但是，该算法的平均车辆分类准确率低于57%，不足以进行交通流监控。本研究提出提高 Yolo 的车辆分类精度，并开发一种新的基于边界框 (Bbox) 的车辆跟踪算法。为此，通过使用从高速公路视频中收集的 123831 个对象模式注释 7216 个图像来准备新的车辆数据集。选择了九个基于机器学习的分类器和一个基于 CNN 的分类器。接下来，通过数据集训练分类器。选择准确率最高的十分之一的分类器与 Yolo 结合。这样，基于 Yolo 的车辆检测器的分类准确率从 57% 提高到 95.45%。车辆检测器 1（Yolo）和车辆检测器 2（Yolo + 最佳分类器），将基于卡尔曼滤波器的跟踪作为车辆跟踪器 1 和基于 Bbox 的跟踪作为车辆跟踪器 2 应用于 4 个高速公路视频的分类/总车辆计数任务。车辆计数结果表明，所开发方法（车辆检测器 2 + 车辆跟踪器 2）的车辆计数精度提高了 13.25%，并且该方法的性能优于本研究中实施的其他 3 个车辆计数系统。