Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations
A new framework for suicide risk assessment using AI-powered video surveillance in metro stations has been introduced. This framework aims to identify high-risk situations by analyzing passenger behavior and spatial context. The study achieved an 83.2% ROC-AUC on real surveillance data, highlighting the complexity of suicide risk assessment.
- ▪The framework assesses suicide risk by incorporating person tracking, activity recognition, and trajectory-driven risk heatmap modeling.
- ▪It formalizes the task of Suicide Risk Assessment (SRA) in metro stations, addressing challenges in human motion perception and behavioral cue aggregation.
- ▪This research opens new directions for interpretable AI systems aimed at social good.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22904 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.22904 (cs) [Submitted on 21 May 2026] Title:Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations Authors:Safwen Naimi, Wassim Bouachir, Guillaume-Alexandre Bilodeau, Brian Mishara View a PDF of the paper titled Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations, by Safwen Naimi and 3 other authors View PDF HTML (experimental) Abstract:Understanding and monitoring human behavior in metro stations play an important role in supporting suicide prevention efforts, where early identification of high-risk situations can enable timely intervention.
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