(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Automated Teller Machines (ATMs) are widely used banking facilities that provide convenient access to financial services. However, these machines are often located in isolated areas with limited human supervision, making them vulnerable to robbery and vandalism. Traditional ATM surveillance systems mainly record video footage and are typically used only for reviewing incidents after they occur. As a result, they are not effective in preventing crimes in real time. To address this limitation, this research proposes ATM-SENTINEL AI, an intelligent video surveillance framework designed to automatically detect suspicious activities related to ATM robbery. The system analyzes surveillance video streams to observe human presence, object interaction, and movement patterns inside ATM booths. By studying behavioral patterns over time, the system distinguishes between normal customer activities and potential robbery attempts. The proposed approach focuses on improving detection reliability while minimizing false alarms caused by regular ATM usage. When suspicious behavior is detected, the system generates real-time alerts to support immediate security response. This intelligent surveillance framework enhances ATM security and improves the efficiency of automated monitoring systems used in modern banking environments