Advanced CCTV Using Machine Learning and Internet of Things

Current CCTV systems mainly act as surveillance tools, often just providing video evidence after an incident has occurred, without the ability to detect threats in real-time or respond automatically. This paper introduces an CCTV system that combines Machine Learning (ML) and Internet of Things (IoT) sensors, shifting from mere monitoring to proactive surveillance. The system employs YOLO V8 algorithms for real-time object detection, recognizing suspicious activities and analyzing behaviors, which helps in crime detection and minimizes response delays. Moreover, it integrates IoT-based environmental sensors—like motion, temperature, and acoustic sensors—to boost context-aware threat detection and automate alert systems.The proposed architecture allows for remote access, sends automated alerts to authorities, and supports intelligent decision-making, paving the way for smarter surveillance in public safety, critical infrastructure, and smart city initiatives. Performance evaluations show a notable increase in threat recognition accuracy and response times compared to traditional CCTV systems