Detection of Unusual Activites at ATM Using Machine Learning

Authors

  • Tejas.D Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Varshini K Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Sushmitha.U Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Sunandha.VK Department of Computer Science, East West Institute of Technology, Bengaluru, India

Keywords:

Unusual Activity, Machine Learning, ATM security

Abstract

The idea of designing and implementation of security against ATM theft is born with the observation of our real life incidents happening around us. This project deals with prevention of ATM crimes and hence overcome the drawback found in existing technology in our society. This paper uses machine learning to enhance the security in ATM. When any suspicious activities such as a man holding a gun in his hand is detected using ORB algorithm, a person attempting to close the camera at ATM, more than two persons entering into ATM, fighting scenes happening at ATM will be detected as an unusual activity and alarm is raised at ATM and a message is passed to nearest police station. Parallelly an email consisting a snap of unusual activity will be sent to the registered police official e-mail id, this helps the police officers to analyze the situation and overcome the fake alarms.

References

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[3] Letian Li,Lin Wu,Yongcun Gao. “Improved image matching method based on ORB”. 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[4] Rublee E, Rabaud V, Konolige K, et al, “ORB: an efficient alternative to SIFT or SURF,” Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE，pp.2564-2571,2011.

[5] Mohan, A. S., & Resmi, R. “Video image processing for moving object detection and segmentation using background subtraction.”2014 First International Conference on Computational Systems and Communications (ICCSC).

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Published

2025-11-26

How to Cite

[1]
T. D, V. K, S. U, and S. VK, “Detection of Unusual Activites at ATM Using Machine Learning”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 213–216, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

License

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