The algorithm for Detecting Intruders at Station Platform Ends Based on Single-line Radar Point Clouds

IF 4.6 Q1 OPTICS
Xiaoshu Wang, Wei Bai, Kaibei Peng
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引用次数: 0

Abstract

Abstract It is a significant concern that there is a risk of passenger intrusions at station platform ends. Current detection uses video triggered by single-line radar, but it is ineffective for accurate identification. In this paper, we address this issue by first analyzing the characteristics of intruders at the ends of train platforms. We propose a two-stage filtering-recognition method to achieve intruder detection based on single-line radar point cloud data. In the first stage, we smooth initial point cloud data using a double-chain exponential weighted moving average filter by grouping points. In the second stage, we extract features using the background subtraction method and a critical threshold of point numbers to detect intruder targets. Experimental results demonstrate that this method is effectively capable of detecting intruders at different distances.
基于单线雷达点云的车站平台端入侵检测算法
摘要车站月台末端存在乘客入侵风险,是一个值得关注的问题。目前的检测采用单线雷达触发的视频,但无法准确识别。在本文中,我们首先通过分析列车站台末端入侵者的特征来解决这一问题。提出了一种基于单线雷达点云数据的两阶段滤波识别方法来实现入侵者检测。在第一阶段,我们使用双链指数加权移动平均滤波器对初始点云数据进行分组平滑。在第二阶段,我们使用背景减法和点数的临界阈值提取特征来检测入侵目标。实验结果表明,该方法能够有效地检测不同距离的入侵者。
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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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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