Real Time Monitoring System for Women’s Privacy Protection in Gender Sensitive Zones

This project presents a Real-Time Monitoring System aimed at enhancing women’s privacy and security in gender-sensitive areas, such as female-only restrooms, changing rooms, and similar spaces. The system integrates computer vision and deep learning techniques to detect unauthorized male entry and trigger immediate alerts. Using Raspberry Pi with a Webcam, real-time video frames are processed to identify gender through a lightweight Deep Learning model. Upon detecting an unauthorized male presence, an automated notification system sends alerts to security personnel. The solution offers scalability, low-cost implementation, and easy integration with existing security infrastructures. Our approach ensures enhanced privacy and security while maintaining operational efficiency.

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Publication Details

Journal
International Research Journal on Advanced Engineering Hub (IRJAEH)
Published
2025-03-28
DOI
https://doi.org/10.47392/irjaeh.2025.0117
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Real Time Monitoring System for Women’s Privacy Protection in Gender Sensitive Zones

Shubham Rajendra Shelke, Pratima Prakash Sawant, Anushka Prakash Shelar, Yash Mohan Shete et al.
1 links
International Research Journal on Advanced Engineering Hub (IRJAEH)
IoT and GPS-based Vehicle Safety Systems
article

Real Time Monitoring System for Women’s Privacy Protection in Gender Sensitive Zones

Shubham Rajendra Shelke, Pratima Prakash Sawant, Anushka Prakash Shelar, Yash Mohan Shete, Umakant Butkar
article en

Abstract

This project presents a Real-Time Monitoring System aimed at enhancing women’s privacy and security in gender-sensitive areas, such as female-only restrooms, changing rooms, and similar spaces. The system integrates computer vision and deep learning techniques to detect unauthorized male entry and trigger immediate alerts. Using Raspberry Pi with a Webcam, real-time video frames are processed to identify gender through a lightweight Deep Learning model. Upon detecting an unauthorized male presence, an automated notification system sends alerts to security personnel. The solution offers scalability, low-cost implementation, and easy integration with existing security infrastructures. Our approach ensures enhanced privacy and security while maintaining operational efficiency.

International Research Journal on Advanced Engineering Hub (IRJAEH)Vol. 3(03)
Guru Gobind Singh Medical College and Hospital (IN)
Openalex Percentile: Top 91%
IoT and GPS-based Vehicle Safety Systems
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