Aerial LiDAR-based 3D Object Detection and Tracking for Traffic Monitoring

Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa, Hichem Besbes, Y. Massoud
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Abstract

The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for object detection and tracking. Due to the unavailability of LiDAR data from the sky, we propose to use a 3D simulator. Then, we implement PointVoxel-RCNN (PV-RCNN) to perform road user detection (e.g., vehicles and pedestrians). Subsequently, we implement an Unscented Kalman filter, which takes a 3D detected object as input and uses its information to predict the state of the 3D box before the next LiDAR scan gets loaded. Finally, we update the measurement by using the new observation of the point cloud and correct the previous prediction's belief. The simulation results illustrate the performance gain (around 8 %) achieved by our solution compared to other 3D point cloud solutions.
基于空中激光雷达的交通监测三维目标检测与跟踪
光探测和测距(LiDAR)技术在汽车行业的普及迅速推动了其在智能城市和物联网等许多新兴领域的应用。与其他传感器(如相机和雷达)相比,LiDAR提供多达64个扫描通道,垂直和水平视野,高精度,高探测范围,以及在恶劣天气条件下的出色性能。本文提出了一种基于光探测与测距(LiDAR)技术的新型空中交通监控方案。通过为无人驾驶飞行器(uav)配备激光雷达传感器,我们可以生成3D点云数据,用于物体检测和跟踪。由于无法获得来自天空的激光雷达数据,我们建议使用3D模拟器。然后,我们实现PointVoxel-RCNN (PV-RCNN)来执行道路使用者检测(例如,车辆和行人)。随后,我们实现了一个Unscented卡尔曼滤波器,该滤波器将3D检测到的物体作为输入,并使用其信息在下一次LiDAR扫描加载之前预测3D盒子的状态。最后,利用点云的新观测值对测量值进行更新,并对先前的预测置信值进行修正。仿真结果表明,与其他3D点云解决方案相比,我们的解决方案实现了性能提升(约8%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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