An Efficient ATM Surveillance Framework Using Optical Flow with CNN

  • Ankit Bisht
  • Himanshu Singh Bisht
  • Vikas TripathiEmail author
Conference paper


For our daily need, we often visit nearby automatic teller machine (ATM) to withdraw our deposited cash in bank. With increase in bank robberies, money snatching and attack on the customer visiting bank or ATM, our money is no longer safe. To prevent these types of abnormal activities, there is a need of better accuracy security surveillance system which can detect abnormal activities instantly. Our paper tends to work in this direction where we have used normal video, dense optical flow and Lucas–Kanade optical flow for detecting the abnormal motion of customer coming for withdrawal of money at ATM booth, with the help of convolution neural network we trained the model for single normal, multiple normal and multiple abnormal classes with different number of iteration to form 15 different training models. While testing those models, the average accuracy for Lucas–Kanade methods for 1000 iteration is 92.3984%.


Optical flow Lucas–Kanade algorithm Dense optical flow Convolutional neural network ATM 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ankit Bisht
    • 1
  • Himanshu Singh Bisht
    • 1
  • Vikas Tripathi
    • 1
    Email author
  1. 1.Graphic Era Deemed to Be UniversityDehradunIndia

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