Machine learning- powered intelligent CCTV for crime detection and proactive security
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Date
2026-06
Journal Title
Journal ISSN
Volume Title
Publisher
AIKTC
Abstract
Traditional CCTV systems depend on continuous human monitoring,
often leading to delayed threat recognition and ineffective responses. This
project presents a Smart CCTV Application Extention that transforms
conventional surveillance into an intelligent, real-time monitoring system
using artificial intelligence and machine learning. The system integrates
advanced computer vision and deep learning models YOLO, CNN, and Lip-
Net to automatically detect threats such as weapon presence, unauthorized
intrusion, and shoplifting, while also interpreting lip movements to identify
potentially harmful verbal cues. Once suspicious activity is detected,
automated alerts are generated to notify relevant authorities for immediate
action. Designed for compatibility with existing CCTV infrastructure,
the system offers a cost-effective and scalable upgrade requiring no hardware
modifications. Experimental evaluations demonstrate high detection
accuracy, efficient real-time processing, and adaptability across diverse environments.
This approach enhances situational awareness, minimizes human
dependency, and represents a significant step toward autonomous and
proactive security systems suitable for both public and private surveillance
applications.