Volume 15, Issue 7 · IJARST · ISSN 2319-1783 / 2320-1126

AI-Powered Proactive Women Safety Monitoring System with Predictive Risk Assessment and Real-Time Police Department Integration

AI-Powered Proactive Women Safety Monitoring System with Predictive Risk Assessment and Real-Time Police Department Integration is an open-access, peer-reviewed research paper by Suganya T, Dharanipriya S V, Hemavathy K, Joshika Sri M, Manasa P, published in Volume 15, Issue 7 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Suganya T, Dharanipriya S V, Hemavathy K, Joshika Sri M, Manasa P

Abstract

Women's safety in public spaces remains a critical global challenge, requiring intelligent, real-time monitoring solutions. This paper presents an AI-powered women safety monitoring system designed to detect high-risk situations and enable rapid emergency response. The proposed system integrates computer vision, deep learning, and real-time alert mechanisms to provide continuous surveillance in educational institutions, workplaces, transportation hubs, and public spaces. The system processes live video feeds using YOLOv8 for human detection and tracking, employs deep neural networks for gender classification, and combines optical flow analysis with MediaPipe-based pose and hand tracking to identify violent behavior and threatening gestures. Real-time risk assessment assigns threat levels Low, Medium, or High based on crowd composition, gender distribution, and detected aggressive actions. When high-risk situations are detected, the system automatically captures incident frames and sends immediate alerts via email and web dashboard with location details and integrated Google Maps links. Additionally, a manual SOS feature enables users to trigger emergency alerts in areas lacking surveillance infrastructure. The system was evaluated through field testing and demonstrated reliable performance in real-time threat detection and alert generation within 3 seconds. This scalable solution enhances women's safety across monitored and unmonitored environments by combining automated video analytics with user-initiated emergency mechanisms.

DOI: https://doi.org/10.62226/ijarst20262747

 

DOI

https://doi.org/10.62226/ijarst20262747

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How do you cite this paper?

Suganya T, Dharanipriya S V, Hemavathy K, Joshika Sri M, Manasa P — “AI-Powered Proactive Women Safety Monitoring System with Predictive Risk Assessment and Real-Time Police Department Integration.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 7. DOI: https://doi.org/10.62226/ijarst20262747.

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