Experimental research on pedestrian detection using LiDAR

Chunxiao Wang; Jingguo Zhu; Tianpeng Xie; Can Zhang; Zhengyu Zhang; Yuyang Zhao

doi:10.1117/12.2603162

31 August 2021 Experimental research on pedestrian detection using LiDAR

Chunxiao Wang, Jingguo Zhu, Tianpeng Xie, Can Zhang, Zhengyu Zhang, Yuyang Zhao

Proceedings Volume 11907, Sixteenth National Conference on Laser Technology and Optoelectronics; 119072R (2021) https://doi.org/10.1117/12.2603162
Event: Sixteenth National Conference on Laser Technology and Optoelectronics, 2021, Shanghai, China

Abstract

Light Detection and Ranging (LiDAR) is gradually developing towards low-cost and high reliability, a variety of highperformance products using different techniques have been derived, which have been widely used in pedestrian detection. In this work, we choose a repetitive scanning LiDAR and a non-repetitive scanning LiDAR to compare the distribution of pedestrian point cloud at different distances. In addition, a pedestrian detection algorithm based on density clustering is designed to compare the detection effects of the two kinds of devices, which provide data support for the research of smart security, V2X (vehicle-to-everything), and autonomous driving. The experiment results show that Livox Horizon has the ability of capturing pedestrian cross-section point cloud with higher completeness and density than Ouster OS1- 64 as integration time increases. Moreover, Horizon and OS1-64 have basically the same detection effect on closedistance dynamic pedestrian, and OS1-64 performs better when detecting pedestrian at 40m. By means of growing integration time, Horizon greatly enhances the ability of detecting long-distance pedestrian.

Citation Download Citation

Chunxiao Wang, Jingguo Zhu, Tianpeng Xie, Can Zhang, Zhengyu Zhang, and Yuyang Zhao "Experimental research on pedestrian detection using LiDAR", Proc. SPIE 11907, Sixteenth National Conference on Laser Technology and Optoelectronics, 119072R (31 August 2021); https://doi.org/10.1117/12.2603162

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available