Advanced Sound Detection and Behavior Examination for Real-Time Intruder Detection using Deep Learning: A Comprehensive Security Framework

Advanced Sound Detection and Behavior Examination for Real-Time Intruder Detection using Deep Learning: A Comprehensive Security Framework

연구 분야: Artificial Intelligence

학회: 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)

The system responds to escalating security concerns with a sophisticated intruder detection system, integrating glass-breaking sound detection and behavior analysis. Using audio sensors and advanced signal processing, it prioritizes detecting glass-breaking events for scenarios involving forced entry through windows or glass doors. Behavior examination and analysis employs deep learning for real-time monitoring, alerting to suspicious activities. Immediate notifications are sent to security personnel or homeowners. Designed with privacy-preserving techniques, it adapts to diverse environments, customizable for various security needs and integrates with existing infrastructure. Representing a significant security advancement, it combines sound detection, behavior analysis, and real-time alerting, prioritizing ethical considerations and privacy preservation. This project paves the way for a safer future.