Motion detection technology has evolved significantly with the integration of artificial intelligence and deep learning approaches in modern security camera systems.This paper presents a comprehensive review of state-of-the-art motion detection technologies, comparing traditional Passive Infrared (PIR) sensors, video-based motion detection, and AI-driven approaches.We analyze system architectures, performance metrics, and implementation challenges across different motion detection paradigms.Experimental results demonstrate that CNN based deep learning models achieve 96% detection accuracy compared to 88% for traditional video motion detection and 82% for PIR sensors.The paper discusses hybrid fusion architectures combining YOLOv8 object detection with optical flow and anomaly detection models, achieving 92.3% accuracy with real-time 30 FPS throughput.Edge computing implementations and cloud-hybrid architectures are examined for their impact on latency, bandwidth, and storage optimization.Key challenges including false positive rates, computational constraints, privacy concerns, and environmental robustness are addressed.Future directions emphasize lightweight models for edge deployment, sensor fusion frameworks, and privacy-preserving architectures for next-generation intelligent surveillance systems.
Rizwan et al. (Thu,) studied this question.