Abstract
Although surveillance cameras are used in ATM cells, we face some problems of robbery and theft at ATMs due to lack of security; however, the monitoring capacity of law enforcement agencies has not kept pace. ATM spoofing attacks can be carried out to break or damage the ATM by stealing the machine and taking cash from the ATM. To reduce this problem, we arm the ATM with a camera module mounted in the room to perform continuous video observation. The camera detects the human and his activity in the ATM and attempts to breach the ATM. It detects unusual activities and immediately sends an alert notification to the police. Therefore, the system handles the application developed to automate video surveillance and detect any potential criminal activity at ATMs. Therefore, in this work, abnormal behavior is observed using CNN and RNN in surveillance videos. These algorithms can be used to recognize faces, detect and track camera movements, and detect and identify the action required to prevent such activity.
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Gnanavel, S., Duraimurugan, N., Jaeyalakshmi, M. (2024). Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning. In: Namasudra, S., Trivedi, M.C., Crespo, R.G., Lorenz, P. (eds) Data Science and Network Engineering. ICDSNE 2023. Lecture Notes in Networks and Systems, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-99-6755-1_11
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DOI: https://doi.org/10.1007/978-981-99-6755-1_11
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