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

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.