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Intelligent Data Engineering and Analytics pp 21–29Cite as

ATM Theft Investigation Using Convolutional Neural Network

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

Image processing in a surveillance video has been a challenging task in research and development for several years. Crimes in Automated Teller Machine (ATM) is common nowadays, in spite of having a surveillance camera inside an ATM as it is not fully integrated to detect crime/theft. On the other hand, we have many image processing algorithms that can help us to detect the covered faces, a person wearing a helmet and some other abnormal features. This paper proposes an alert system, by extracting various features like face-covering, helmet-wearing inside an ATM system to detect theft/crime that may happen. We cannot judge theft/crime as it may happen at any time but we can alert the authorized persons to monitor the video surveillance.

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Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka Surathkal, Mangaluru, 575025, India

    Y. C. Satish & Bhawana Rudra

Authors
  1. Y. C. Satish
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  2. Bhawana Rudra
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Corresponding author

Correspondence to Y. C. Satish .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

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Satish, Y.C., Rudra, B. (2021). ATM Theft Investigation Using Convolutional Neural Network. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_3

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