The rapid expansion of unmanned retail stores has raised critical security concerns, thereby necessitating the development and implementation of robust protective measures. The absence of real-time monitoring systems in these environments has heightened the vulnerability to risks such as theft and property damage. Although closed-circuit television (CCTV) systems have been deployed to retrospectively investigate criminal activities, these systems are often insufficient in preventing incidents. This study introduces a Transformer-based intelligent CCTV system designed for the real-time detection of anomalous behaviors within unmanned retail environments. Unlike conventional systems that rely on basic machine learning models, our proposed system leverages human joint position data extracted from CCTV footage to classify a range of anomalous behaviors, including theft, falls, and property damage. Additionally, extensive hyperparameter optimization was performed to maximize the model's effectiveness in these specific environments. Our System enhances the system's usability by enabling real-time identification of anomalous behavior, complete with location data, timestamps, and corresponding video frame sequences.

Transformer-based Intelligent CCTV System for Real-time Anomaly Detection in Unmanned Stores