用视频分析地铁乘客行为，提前识别自杀风险
Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations
- 融合轨迹、动作与环境信息综合评估风险
- 在真实数据上达到83.2%的ROC-AUC表现
- 可解释框架助力公共安全干预决策
地铁站中人类行为的理解与监测对自杀预防至关重要，早期识别高风险情境可实现及时干预。这需要从监控视频中联合推理每位乘客的行为、空间位置及时间动态。然而，利用监控摄像头捕捉的视频进行此类评估极具挑战性，因需精准感知人体运动、理解站台几何结构，并随时间聚合异构行为线索。本文首次形式化了地铁站自杀风险评估（SRA）任务，提出首个可解释的解决方案。不同于仅关注单一子任务或直接推断意图的方法，本框架通过人迹跟踪、活动识别、站台语义分割和基于轨迹的风险热力图建模，综合累积证据评估风险。通过将SRA定义为独立任务并建立完整流水线，在真实监控数据上实现83.2%的ROC-AUC，凸显其复杂性，并为社会向好型可解释AI研究开辟新方向。
原文摘要 · Abstract (English)
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. This requires assessing suicide risk from a surveillance video by jointly reasoning about the behavior of each passenger, his/her spatial context, and temporal dynamics. However, this assessment using videos captured by surveillance cameras is challenging, as it demands accurate perception of human motion, understanding of platform geometry, and aggregation of heterogeneous behavioral cues over time. In this work, we formalize the task of Suicide Risk Assessment (SRA) in metro stations and introduce the first interpretable framework that addresses this challenge. Unlike approaches that focus on isolated subtasks or attempt to infer intent directly, our formulation assesses suicide risk from accumulated evidence by incorporating person tracking, activity recognition, semantic segmentation of the platform, and trajectory-driven risk heatmap modeling. By formalizing SRA as a distinct task and benchmarking a complete operational pipeline achieving 83.2% ROC-AUC on real surveillance data, this work highlights the complexity of suicide risk assessment and opens new directions for research on interpretable AI systems for social good.
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