DEVELOPMENT OF A REAL-TIME AND RETROSPECTIVE INTELLIGENT OBJECT DETECTION SYSTEM FROM CCTV FOR SECURITY ENHANCEMENT IN LOCAL GOVERNMENT ORGANIZATIONS

This research develops an AI-RTRO system to address the limitations of traditional CCTV in Bang Yai Municipality's Smart Safety Infrastructure. The system improves real-time situational awareness and enhances retrospective evidence retrieval for local government. Managing large-scale CCTV data and making rapid decisions are key operational challenges. The system integrates the AI Agent Core framework and IPOF model for object detection, tracking, and retrieval from both real-time and archived video. Deep learning algorithms (YOLO, DeepSORT/ByteTrack, ResNet, Faiss) are used, with Grad-CAM providing transparency. Performance is evaluated in real-world conditions with mAP, IDF1, and latency. Results show accurate detection, stable tracking, and low-latency operation. Metadata management and feature vector indexing improve the performance of retrospective retrieval. The study confirms that integrating AI Agent Core and IPOF improves system performance, retrieval efficiency, decision transparency, and continuous learning. The framework was rated highly by 15 experts, with a mean of 4.74 (S.D. = 0.55), indicating readiness for practical use in local government surveillance.