Detection of Suspicious Activity in ATM Using Deep Learning

Project Description & Abstract

ATM Suspicious Activity Detection Using Deep Learning is an intelligent security and surveillance system designed to identify potentially suspicious or abnormal activities occurring inside or around Automated Teller Machines (ATMs). The system uses Artificial Intelligence, Deep Learning, Computer Vision, and Image Processing techniques to analyze images or video streams captured through surveillance cameras. Traditional ATM surveillance systems generally depend on continuous monitoring of CCTV footage by security personnel. Monitoring a large number of cameras manually can be difficult, time-consuming, and may result in important events being missed. The proposed system addresses this challenge by automatically analyzing camera footage and identifying activities that may require immediate attention. The system processes images or real-time video obtained from an ATM surveillance camera and applies Deep Learning-based object detection and image classification techniques to identify suspicious objects or activities. One of the major applications described in the research is weapon detection, where the system can identify objects such as weapons appearing within the monitored ATM environment. When a potentially suspicious event is detected, the system can generate an alert or notification, allowing security personnel or authorized authorities to respond quickly. The system can therefore act as an intelligent monitoring layer rather than requiring security personnel to continuously observe every CCTV camera. Deep learning models can automatically learn important visual features from training images, making them suitable for detecting objects and abnormal activities under different environmental conditions. The system can be trained using a dataset containing normal ATM scenes and suspicious-event or weapon images. The proposed application can be extended to support real-time video monitoring, suspicious behavior detection, weapon detection, unauthorized access detection, emergency alerts, and security-event logging. This makes it suitable for intelligent surveillance applications in ATMs and other restricted or high-security environments. Overall, the project demonstrates how deep learning and computer vision can be applied to automated security monitoring, helping reduce dependence on continuous manual CCTV surveillance and enabling faster detection of potentially dangerous situations. Tags ATM Security, ATM Suspicious Activity Detection, ATM Crime Detection, ATM Surveillance, Suspicious Activity Detection, Deep Learning, Computer Vision, AI Surveillance, Weapon Detection, Object Detection, CCTV Monitoring, Video Surveillance, Image Processing, CNN, YOLO, OpenCV, TensorFlow, Artificial Intelligence, Security System, Smart Surveillance, Real-Time Detection, Banking Security, Crime Detection, Python, Flask