# Dual Weakly Supervised Anomaly Detection and Unsupervised Segmentation for Real-Time Railway Perimeter Intrusion Monitoring

## TL;DR

This paper introduces an intelligent monitoring system for detecting foreign objects on high-speed train tracks using video analysis and machine learning techniques.

## Contribution

The novel approach combines weakly supervised anomaly detection with unsupervised segmentation for real-time railway intrusion monitoring.

## Key findings

- The video anomaly detection model achieved an AUC of 0.99 on the track anomaly detection dataset.
- The foreground segmentation algorithm achieved an F1 score of 0.9030 on the track anomaly dataset.
- The system processes video segments at 91 Frames per Second, demonstrating high efficiency.

## Abstract

The high operational velocities of high-speed trains present constraints on their onboard track intrusion detection systems for real-time capture and analysis, encompassing limited computational resources and motion image blurring. This emphasizes the critical necessity of track perimeter intrusion monitoring systems. Consequently, an intelligent monitoring system employing trackside cameras is constructed, integrating weakly supervised video anomaly detection and unsupervised foreground segmentation, which offers a solution for monitoring foreign objects on high-speed train tracks. To address the challenges of complex dataset annotation and unidentified target detection, weakly supervised learning detection is proposed to track foreign object intrusions based on video. The pretraining of Xception3D and the integration of multiple attention mechanisms have markedly enhanced the feature extraction capabilities. The Top-K sample selection alongside the amplitude score/feature loss function effectively discriminates abnormal from normal samples, incorporating time-smoothing constraints to ensure detection consistency across consecutive frames. Once abnormal video frames are identified, a multiscale variational autoencoder is proposed for the positioning of foreign objects. A downsampling/upsampling module is optimized to increase feature extraction efficiency. The pixel-level background weight distribution loss function is engineered to jointly balance background authenticity and noise resistance. Ultimately, the experimental results indicate that the video anomaly detection model achieved an AUC of 0.99 on the track anomaly detection dataset and processes 2 s video segments in 0.41 s. The proposed foreground segmentation algorithm achieved an F1 score of 0.9030 in the track anomaly dataset and 0.8375 on CDnet2014, with 91 Frames per Second, confirming its efficacy.