Formalizes suicide risk assessment (SRA) from metro videos and benchmarks an interpretable pipeline with 83.2% ROC-AUC using tracking, activity recognition, segmentation, and risk heatmaps.
Using deep learning model integration to build a smart railway traffic safety monitoring system.Scientific Re- ports, 15
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations
Formalizes suicide risk assessment (SRA) from metro videos and benchmarks an interpretable pipeline with 83.2% ROC-AUC using tracking, activity recognition, segmentation, and risk heatmaps.