ABSTRACT A vehicle extraction method is proposed in this paper to extract vehicles in urban areas more accurately from airborne point clouds. First, the ground points are separated from the non-ground points, and a potential vehicle-occupied area (PVOA) is then extracted from the ground point cloud. A PVOA-based non-ground point cloud segmentation method is proposed in this work, and a gap-based method is put forward to re-cluster the segment, which may include multiple vehicles. The non-ground point cloud is clustered into a series of one-vehicle segments and empty segments. Following this, a shape-based vehicle recognition method is presented that can judge whether or not a given segment is a vehicle using a dynamic time warping similarity measurement. In addition to judging whether or not a segment is a vehicle, the category of each vehicle can also be determined. To a significant extent, our PVOA-based non-ground point cloud segmentation method can avoid the difficulties of over- and under-segmentation that arise in current mainstream methods of object-based vehicle extraction, and can also avoid incorrect segmentation in case of vehicles parked close together. Our shape-based vehicle recognition method can exclude non-vehicle objects, especially those with sizes similar to those of vehicles. This method is more effective than current algorithms based on normal geometric features such as area and rectangularity. Using four datasets of typical urban scenarios, the performance of the new algorithm is tested and compared with that of the OBPCA and DT algorithm. The experimental results show that the correctness, completeness, and quality of the new algorithm are 96.7%, 91.1%, and 93.8%, higher than that of the OBPCA and DT algorithm.

中文翻译：

---

从航空激光扫描点云数据中提取城市车辆

摘要 本文提出了一种车辆提取方法，以从空中点云中更准确地提取城市地区的车辆。首先，地面点与非地面点分离，然后从地面点云中提取潜在车辆占用区域（PVOA）。本文提出了一种基于PVOA的非地面点云分割方法，并提出了一种基于间隙的方法来重新聚类可能包括多辆车的线段。非地面点云被聚类为一系列单车段和空段。在此之后，提出了一种基于形状的车辆识别方法，该方法可以使用动态时间扭曲相似度测量来判断给定路段是否为车辆。除了判断一个segment是否是一个车辆，还可以确定每辆车的类别。我们的基于 PVOA 的非地面点云分割方法在很大程度上可以避免当前基于对象的车辆提取主流方法中出现的过分割和欠分割的困难，也可以避免在车辆的情况下出现错误分割停在一起。我们基于形状的车辆识别方法可以排除非车辆物体，尤其是那些与车辆尺寸相似的物体。这种方法比目前基于正常几何特征（如面积和矩形）的算法更有效。使用典型城市场景的四个数据集，对新算法的性能进行了测试，并与OBPCA和DT算法的性能进行了比较。实验结果表明，正确性、完整性、