The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge due to the limitations of current vehicle detection techniques, as well as the various camera conditions such as height and resolution. In this work, a quadtree based algorithm is developed to continuously partition the image extent until only regions with high detection accuracy are remained. These regions are referred to as the high-accuracy identification regions (HAIR) in this paper. We demonstrate how the use of the HAIR can improve the accuracy of traffic density estimates using images from traffic cameras at different heights and resolutions in Central Ohio. Our experiments show that the proposed algorithm can be used to derive robust HAIR where vehicle detection accuracy is 41 percent higher than that in the original image extent. The use of the HAIR also significantly improves the traffic density estimation with an overall decrease of 49 percent in root mean squared error.
翻译:城市地区实时摄像材料数量不断增加,使得有可能为有效的交通规划、操作和管理提供高质量的交通数据。然而,由于目前车辆探测技术的局限性,以及高度和分辨率等各种摄像条件的限制,从这些摄像材料中得出可靠的交通量指标是一项挑战。在这项工作中,以四叶为基础的算法可以持续分割图像范围,直到只有检测精确度较高的区域才能留下。本文中将这些区域称为高准确度识别区域。我们展示使用HAIR如何利用中俄亥俄州不同高度和分辨率的交通摄像头图像提高交通密度估计的准确性。我们的实验表明,在车辆探测精确度比原始图像精确度高41%的情况下,可以使用拟议的算法来得出稳健的HAIR。使用HAIR还大大改进了交通密度估计,从而总体减少了49%的根正方位误差。