论文标题
对象检测和跟踪车辆计数的算法：比较分析
Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis
论文作者
论文摘要
深度学习和高性能计算领域的快速发展极大地增强了基于视频的车辆计数系统的范围。在本文中，作者部署了几种最先进的对象检测和跟踪算法，以检测和跟踪其感兴趣区域（ROI）中的不同类别的车辆。正确检测和跟踪车辆在投资回报率中的目标的目的是获得准确的车辆数量。对象检测模型的多种组合和不同的跟踪系统的多种组合用于访问最佳的车辆计数框架。这些模型解决了与不同天气条件，遮挡和弱光设置相关的挑战，并通过其计算丰富的训练和反馈周期有效地提取了车辆信息和轨迹。所有模型组合产生的自动车辆计数均经过验证，并与从路易斯安那州运输和开发部获得的9个小时的交通视频数据进行了比较。实验结果表明，Centernet和Deep Sort，Dintectron2和Deep Sort以及Yolov4和Deep Sort的组合构成了所有车辆的最佳总体计数百分比。
The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video based vehicle counting system. In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles' in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models' addresses challenges associated to different weather conditions, occlusion and low-light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 hours' traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, Detectron2 and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.
