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 Point Voxel-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.
B. Cherif et al., "Aerial LiDAR-based 3D Object Detection And Tracking For Traffic Monitoring," Proceedings - IEEE International Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/ISCAS46773.2023.10181371
Electrical and Computer Engineering
Keywords and Phrases
deep learning; detection; LiDAR; tracking; Traffic monitoring; UAV
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
01 Jan 2023