基于3D背景地图和跟踪的城市场景多目标检测

Orkény Zováthi, L. Kovács, Balázs Nagy, C. Benedek
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摘要

本文提出了一种基于旋转多波束激光雷达测量的实时三维动态目标检测方法,该方法利用存储在新一代地理信息系统(GIS)中的三维背景城市地图和通过跟踪传播的先前检测到的动态目标,改进了实时三维动态目标检测方法。首先,我们采用最先进的目标检测方法,并区分预测的动态目标候选区域和当前激光雷达测量的剩余静态区域。接下来,我们使用在霍夫空间中操作的多模态点云配准算法,找到了RMB激光雷达测量的静态部分与背景城市地图之间的最优转换。在精确对准后,我们基于地图对RMB激光雷达数据中的候选伪阳性检测目标进行过滤。为了找到当前测量中被目标检测器遗漏的额外目标,我们应用了基于卡尔曼滤波的目标跟踪。因此,我们首先预测先前检测和跟踪的目标的当前状态。接下来,我们在跟踪对象和当前对象之间应用基于匈牙利匹配器的赋值,并根据结果更新对象列表。为了提高准确性,我们将所有的预测都保存在几个帧中。我们在匈牙利布达佩斯拥挤的城市场景中定性和定量地评估了我们的方法,结果表明,与纯粹依赖单一激光雷达时间框架的最先进的物体检测方法相比,基于背景地图的滤波在检测车辆和行人的精度方面可以提高26.52%,提高9.38%,而通过跟踪,车辆和行人的召回率分别提高12.84%和14.34%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Object Detection in Urban Scenes Utilizing 3D Background Maps and Tracking
In this paper we propose a novel approach for upgrading real time 3D dynamic object detection methods operating on rotating multi-beam (RMB) Lidar measurements using 3D background city maps stored in new generation geographic information systems (GIS) and previously detected dynamic objects propagated by tracking. First, we apply a state-of-the-art object detection method and distinguish the predicted dynamic object candidates and the remaining static regions of the current Lidar measurement. Next we find an optimal transformation between the static part of the RMB Lidar measurements and the background city map using a multimodal point cloud registration algorithm operating in the Hough space. After the accurate alignment, we filter false-positively detected object candidates in the RMB Lidar data based on the map. To find additional objects missed by the object detector on the current measurement, we apply a Kalman-filter based object tracking. Hereby we first predict the current state of the previously detected and tracked objects. Next, we apply a Hungarian matcher based assignment between the tracked and the current objects and update the object list according to the result. For better accuracy, we keep all predictions through a couple of frames. We evaluated our method qualitatively and quantitatively in crowded urban scenes of Budapest, Hungary, and the results showed that with background map based filtering we can achieve a 26,52% improvement detecting vehicles and 9,38% for pedestrians in precision, while via tracking, a 12,84% improvement for vehicles and 14,34% for pedestrians in recall against the state-of-the-art object detection method relying purely on a single Lidar time frame.
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