Pedestrian detection method based on LIDAR sensors

被引：0
|
作者：
Han X. [1 ]
Lu J. [1 ]
Li X. [1 ]
Zhao C. [1 ]
机构：
[1] School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2019年 / 40卷 / 06期
关键词：
Autonomous driving; Environment understanding; Fast point feature histograms(FPFH); LIDAR; Object detection; Pedestrian detection; Support vector machine (SVM); Unmanned ground vehicle;
D O I：
10.11990/jheu.201803119
中图分类号：
学科分类号：
摘要：
Given that most current features based on LIDAR point clouds cannot describe the shape of pedestrian distribution, we propose a pedestrian detection method for unmanned ground vehicles. Our proposed method is based on LIDAR sensor orientation. We clustered non-ground LIDAR point clouds via DBSCAN and propose the features of the distribution of fast-point feature histograms (DFPFH) needed to train a support vector machine(SVM) classifier for pedestrian detection. The accuracy and effectiveness of the method were tested by using the KITTI OBJECT dataset and an unmanned ground vehicle. Results proved that in contrast to LIDAR-based features, DFPFH features can improve the performance of pedestrian detection effectively and can meet the real time requirement of pedestrian detection for unmanned ground vehicles. © 2019, Editorial Department of Journal of HEU. All right reserved.
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页码：1149 / 1154
页数：5
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