Abstract
Automated teller machines (ATM) are widely being used to carry out banking transactions and are becoming one of the necessities of everyday life. ATMs facilitate withdrawal, deposit, and transfer of money from one account to another round the clock. However, this convenience is marred by criminal activities like money snatching and attack on customers, which are increasingly affecting the security of bank customers. In this paper, we propose a video based framework that efficiently identifies abnormal activities happening at the ATM installations and generates an alarm during any untoward incidence. The proposed approach makes use of motion history image (MHI) and Hu moments to extract relevant features from video. Principle component analysis has been used to reduce the dimensionality of features and classification has been carried out by using support vector machine. Analysis has been carried out on different video sequences by varying the window size of MHI. The proposed framework is able to distinguish the normal and abnormal activities like money snatching, harm to the customer by virtue of fight, or attack on the customer with an average accuracy of 95.73%.
Highlights
Automated teller machines (ATM) is a computerized telecommunication device that serves the customer of a financial firm with a swift access to financial transactions in a public space by exempting the need for a clerk or bank teller
The system is trained using these videos for different number of motion history image (MHI) frames (Table 1)
The system was tested for different number of MHI frames (5, 10, and 15) (Table 2)
Summary
ATM is a computerized telecommunication device that serves the customer of a financial firm with a swift access to financial transactions in a public space by exempting the need for a clerk or bank teller. We need an advanced system that can effectively monitor and automatically recognize unusual crime activities in an ATM room and can report to the nearest monitoring firm before an offender could elope. Another approach to handle this situation could be an alarm system or electrical buzzer. The system can automatically recognize different actions or number of persons through a CCTV camera like single normal, multiple normal, and multiple abnormal and generate signal .
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