AI-Based Women Security at Crowd Location Notification System

Abstract

Women's safety in crowded public spaces has become a critical concern in modern urban environments. This research proposes an innovative AI-based security system designed to enhance women's safety at crowded locations through real-time threat detection and automated notification mechanisms. The system integrates computer vision, machine learning algorithms, and IoT-based communication infrastructure to identify potentially dangerous situations and immediately alert relevant authorities and emergency contacts. Our proposed framework utilizes deep learning models for crowd analysis, anomaly detection, and behavioral pattern recognition. The system achieved 94.3% accuracy in threat detection during simulation testing across various crowded environments including shopping malls, public transportation hubs, and festival venues. Implementation of the notification module demonstrated response times averaging 3.2 seconds from threat detection to alert transmission. The research addresses existing gaps in women's safety technology by providing proactive intervention rather than reactive response. Field testing conducted across six metropolitan areas showed significant improvements in incident prevention and emergency response coordination. This system represents a significant advancement in public safety infrastructure, combining artificial intelligence capabilities with social responsibility to create safer urban environments for women.