论文标题
使用透视转换来检测3D界箱的车辆盒，以进行准确的速度测量
Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement
论文作者
论文摘要
通过交通监视摄像机捕获的车辆的检测和跟踪是智能运输系统的关键组成部分。我们提出了一种改进的算法版本，用于检测3D边界车辆的车辆，其跟踪和随后的速度估计。我们的算法利用了监视场景中已知的消失点的几何形状来构建视角转换。该转换可以使用标准2D对象检测器使用一个附加参数来检测3D边界框的问题直观简化2D边界框。本文的主要贡献是改进的透视转换构造，该转换更加稳健，全自动，并且对速度估计进行了扩展的实验评估。我们测试了BRNOCOMPSPEED数据集的速度估计任务的算法。我们通过不同的配置评估我们的方法，以衡量在2D检测上检测3D边界盒检测的准确性和计算成本之间的关系。所有测试的配置实时运行，并且是全自动的。与其他已发表的最先进的全自动结果相比，我们的算法将平均绝对速度测量误差降低32％（1.10 km/h至0.75 km/h），绝对中值误差降低了40％（0.97 km/h至0.58 km/h）。
Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation. The transformation enables an intuitive simplification of the problem of detecting 3D bounding boxes to detection of 2D bounding boxes with one additional parameter using a standard 2D object detector. Main contribution of this paper is an improved construction of the perspective transformation which is more robust and fully automatic and an extended experimental evaluation of speed estimation. We test our algorithm on the speed estimation task of the BrnoCompSpeed dataset. We evaluate our approach with different configurations to gauge the relationship between accuracy and computational costs and benefits of 3D bounding box detection over 2D detection. All of the tested configurations run in real-time and are fully automatic. Compared to other published state-of-the-art fully automatic results our algorithm reduces the mean absolute speed measurement error by 32% (1.10 km/h to 0.75 km/h) and the absolute median error by 40% (0.97 km/h to 0.58 km/h).
