Real-Time Intelligence Surveillance Using Object Detection and Facial Recognition on Edge Devices

This research presents a lightweight AI system for real-time object detection and facial recognition on edge computing platforms like the Raspberry Pi. By integrating YOLOv8 for object detection and DeepFace for facial analysis with OpenCV, the system performs efficient, offline inference on resource-constrained devices without relying on cloud infrastructure. A custom domain-specific dataset enhances detection precision and recognition accuracy. Designed for forensic, surveillance, and law enforcement applications, the unified architecture enables low-latency, privacy-preserving analysis directly on-site. The modular design supports diverse scenarios such as intelligent surveillance, suspect identification, and demographic analysis. DeepFace extends functionality by enabling real-time face detection, emotion recognition, and demographic estimation (age, gender) from live camera feeds. The compact deployment demonstrates how artificial intelligence can operate effectively on edge devices for time-sensitive, real-world applications. This work emphasizes the role of modern AI models, privacy-aware offline systems, and tailored datasets in improving accuracy. It establishes a scalable foundation for broader applications in intelligent visual monitoring and forensic investigations, showing how edge AI can support secure and efficient decision-making in evolving security landscapes.