Security Reinforcement for ATM by Using Image Sensing

Abstract

ATM (Automated Teller Machine)-related crimes have been reported as serious problems overseas. One of such crimes is skimming: the act of installing skimming device on ATM to illegally copy information of cash cards or passwords. Detecting such illegal acts promptly is important since we can immediately prevent ATM-related crimes by automatically raising alarms and also reduce supervisor’s monitoring burden. We have realized an anomaly behavior detection technique which finds not only suspicious devices but also detects actions of installing them. In this article, we discuss our proposed method which combines video feature extraction, ATM status acquisition and recognition. Compared to processing only image information, the proposed method proves to be more effective in recognizing anomaly behaviors from normal behaviors.