Vehicle target classification algorithm based on roadside 3D LiDAR

YANG Siyuan; ZHENG Jianying

doi:10.13873/J.1000-9787(2020)07-0123-04

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Vehicle target classification algorithm based on roadside 3D LiDAR

更新时间：2025-09-11

- Vehicle target classification algorithm based on roadside 3D LiDAR
- Vol. 39, Issue 7, Pages: 123-126(2020)
- 作者机构：
  
  苏州大学轨道交通学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)07-0123-04
  CLC： U495;TN958.98;TP391.41
- Published：2020
- 稿件说明：
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[1]杨思远,郑建颖.基于路侧三维激光雷达的车辆目标分类算法[J].传感器与微系统,2020,39(07):123-126.

YANG Siyuan, ZHENG Jianying. Vehicle target classification algorithm based on roadside 3D LiDAR[J]. 2020, 39(7): 123-126.
[1]杨思远,郑建颖.基于路侧三维激光雷达的车辆目标分类算法[J].传感器与微系统,2020,39(07):123-126. DOI： 10.13873/J.1000-9787(2020)07-0123-04.

YANG Siyuan, ZHENG Jianying. Vehicle target classification algorithm based on roadside 3D LiDAR[J]. 2020, 39(7): 123-126. DOI： 10.13873/J.1000-9787(2020)07-0123-04.

摘要

以三维激光雷达路侧部署为技术手段

提出了一种适用于城市复杂环境的车辆目标分类算法。该算法首先使用背景差分法和密度聚类算法实现了车辆点云的检测

然后提取车辆点云的形态、比例、高度轮廓、侧面轮廓等特征

训练随机森林分类器

实现了车辆目标的准确分类。最终分析比较了单特征和综合特征的检测分类性能。结果表明:该算法在城市复杂环境下对车辆目标分类准确率达到了95. 2%。

Abstract

A classification algorithm for vehicle targets applied to complex urban environment is proposed using 3D laser infrared radar（LiDAR） roadside deployment. Firstly

background difference method and density clustering algorithm are used to detect vehicle point clouds. Then

the shape

proportion

height profile and profile of vehicle point clouds are extracted

and Random Forest classifier is trained to achieve accurate classification of vehicle targets. Finally

the detection and classification performances of single feature and comprehensive feature are analyzed and compared. The results show that the accuracy rate of vehicle target classification in urban complex environment is 95. 2 %.

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references

基于背景差分法的模型改进方法研究 [J]. 宋伟,张力,邓亚航. 传感器与微系统 . 2017(10)

城市复杂环境下基于三维激光雷达实时车辆检测 [J]. 程健,项志宇,于海滨,刘济林. 浙江大学学报(工学版) . 2014(12)

连续帧间差分与背景差分相融合的运动目标检测方法 [J]. 屈晶晶,辛云宏. 光子学报 . 2014(07)

The Image Process Based on Intelligent Transportation System [J] . Wen Qian An,Xu Fang Bo,Chao Jia. Advanced Materials Research . 2014 (945)

An Efficient Method To Estimate Bagging’s Generalization Error [J] . David H. Wolpert,William G. Macready. Machine Learning . 1999 (1)

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