Unusual Human Activity Recognition in ATM’S

Abstract

Abstract: This paper addresses the imperative need to enhance Automated Teller Machine (ATM) safetyand security through the integration of deep learning and OpenCV. Leveraging Convolutional Neural Networks (CNNs) for feature extraction and Siamese Networks for anomaly detection, our approach aims to fortify ATM systems against threats like skimming and unauthorized access. Realtime image processing with OpenCV facilitatesefficient preprocessing. The study critically examines existing methodologies, highlighting vulnerabilities to adversarial attacks and environmental variations. Through rigorous evaluation and adversarial testing, this research of ATM security systems, fostering a more secure financial landscape.