To detect abnormal event at ATM system by using image processing based on IOT technologies

  • Authors

    • Kande Archana ASST.PROFESSOR
    • P Bhaskara Reddy Director
    2018-06-14
    https://doi.org/10.14419/ijet.v7i3.11773
  • Use GSM and GPS, LCD, DC and Stepper Motor, Vibration sensor, ARM controller.

  • Now a day’s ATMs are equipped with money there is possibility of robberies. This paper proposes a framework which will provide high security in ATMs. The Prototype includes ARM controller, Vibration Sensor, GSM and GPS Technique, DC Motor, Stepper motor, Buzz-er, LCD Display, and Keil Tool. Whenever robbery occurs, Vibration sensor is used here which senses vibration produced from ATM machine. This system uses ARM controller based embedded system to process real time data collected using the vibration sensor. Once the vibration is sensed the beep sound will occur from the buzzer. DC Motor is used for closing the door of ATM.

    Stepper motor is used to leak the gas inside the ATM to bring the thief into unconscious stage. Camera is always in processing and sending video continuous to the PC and it will be saved in computer. RTC used to capture the robber occur time and sends the SMS and MMS to the nearby police station and corresponding bank through the GSM and GPS. Here LCD displays board using showing the output of the message continuously. This will prevent the robbery and the person involving in robbery can be easily caught. Here, Keil tools are used to implement the idea and results are obtained. keil tools is used for run the DC motor and stepper motor for automatic door lock and also leak the gas inside the ATM. By this system robberies will be stopped and the complaints cases also reduced maximally. Thus the proposed framework results are revealed that the framework can be providing high security to the ATM System's

     

  • References

    1. [1] M. S. Scott, Robbery at Automated Teller Machines, US Department of Justice, Office of Community Oriented Policing Services, 2001.

      [2] N. Sharma, “Analysis of different vulnerabilities in auto teller machine transactions,†Journal of Global Research in Computer Science, pp. 38–40, 2012.

      [3] R. S. Shirbhate, N. D. Mishra, and R. P. Pande, “Video surveillance system using motion detection: a survey,†International Journal Advanced Networking and Applications, vol. 3, no. 5, pp. 19–22, 2012.

      [4] IBN LIVE, 2014, ttp://ibnlive.in.com/news/bangalore-atmattack- womans-skull-fractured-still-in-icu/435190-62-129.html.

      [5] NDTV, 2014, http://www.ndtv.com/article/cities/bangalore-thisbrave- atm-guard-grabs-machete-from-robbers-hits-one-ofthem- 465052?curl=1422275786.

      [6] MAN ATTACKED, January 2014, http://www.youtube.com/ watch?v=RGaupYx2fpQ.

      [7] R. Poppe, “Asurvey on vision-based human action recognition,†Image and Vision Computing, vol. 28, no. 6, pp. 976–990, 2010. https://doi.org/10.1016/j.imavis.2009.11.014.

      [8] U. Mahbub,H. Imtiaz, andM. A. R. Ahad, “Action recognition based on statistical analysis fromclustered flow vectors,†Signal, Image and Video Processing, vol. 8, no. 2, pp. 243–253, 2014. https://doi.org/10.1007/s11760-013-0533-3.

      [9] W. Gong, J. Gonz`alez, and F. X. Roca, “Human action recognition based on estimated weak poses,†EURASIP Journal on Advances in Signal Processing, vol. 2012, article 162, 2012.

      [10] M. Paul, S. M. Haque, and S. Chakraborty, “Human detection in surveillance videos and its applications—a review,†EURASIP Journal on Advances in Signal Processing, vol. 2013, article 176, 2013.

      [11] W. Kim, J. Lee, M. Kim, D. Oh, and C. Kim, “Human action recognition using ordinal measure of accumulated motion,†EURASIP Journal on Advances in Signal Processing, vol. 2010, Article ID 219190, 2010. https://doi.org/10.1155/2010/219190.

      [12] M. Ahmad and S.-W. Lee, “Human action recognition using shape and CLG-motion flow from multi-view image sequences,†Pattern Recognition, vol. 41, no. 7, pp. 2237–2252, 2008. https://doi.org/10.1016/j.patcog.2007.12.008.

      [13] J. W. Davis and A. F. Bobick, “The representation and recognition of human movement using temporal templates,†in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 928–934, June 1997. https://doi.org/10.1109/CVPR.1997.609439.

      [14] M. A. R. Ahad, J. K. Tan, H. Kim, and S. Ishikawa, “Motion history image: its variants and applications,†Machine Vision and Applications, vol. 23, no. 2, pp. 255–281, 2012. https://doi.org/10.1007/s00138-010-0298-4.

      [15] M.-K. Hu, “Visual pattern recognition by moment invariants,†IRE Transactions on Information Theory, vol. 8, no. 2, pp. 179– 187, 1962. https://doi.org/10.1109/TIT.1962.1057692.

      [16] A. F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,†IEEE Transactions on Pattern Analysis andMachine Intelligence, vol. 23,no. 3, pp. 257– 267, 2001.

      [17] A. Amato and V. D. Lecce, “Semantic classification of human behaviors in video surveillance systems,†WSEAS Transactions on Computers, vol. 10, no. 10, pp. 343–352, 2011.

      [18] Q. Chen, R. Wu, Y. Ni, R. Huan, and Z. Wang, “Research on human abnormal behavior detection and recognition in intelligent video surveillance,†Journal of Computational Information Systems, vol. 9, no. 1, pp. 289–296, 2013.

      [19] P. Srestasathiern and A. Yilmaz, “Planar shape representation and matching under projective transformation,†Computer Vision and Image Understanding, vol. 115, no. 11, pp. 1525–1535, 2011. https://doi.org/10.1016/j.cviu.2011.07.004.

      [20] S. Bourennane and C. Fossati, “Comparison of shape descriptors for hand posture recognition in video,†Signal, Image and Video Processing, vol. 6, no. 1, pp. 147–157, 2012. https://doi.org/10.1007/s11760-010-0176-6.

      [21] G. Debard, P. Karsmakers, M. Deschodt et al., “Camera based fall detection using multiple features validated with real life video,†in Intelligent Environments Workshops, vol. 10, pp. 441– 450.

      [22] E. B. Nievas, O. D. Suarez, G. B. Garcia, and R. Sukthankar, “Violence detection in video using computer vision techniques,†in Computer Analysis of Images and Patterns, vol. 6855, pp. 332– 339, Springer, Berlin, Germany, 2011. https://doi.org/10.1007/978-3-642-23678-5_39.

      [23] H. Wang, A. Finn, O. Erdinc, and A. Vincitore, “Spatialtemporal structural and dynamics features for Video Fire

      [24] Detection,†in Proceedings of the IEEEWorkshop on Applications of Computer Vision (WACV ’13), pp. 513–519, Tampa, Fla, USA, January 2013.

      [25] S. Oh, A. Hoogs, A. Perera et al., “A large-scale benchmark dataset for event recognition in surveillance video,†in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’11), pp. 3153–3160, Providence, RI, USA, June 2011. https://doi.org/10.1109/CVPR.2011.5995586.

      [26] P. Rota, N. Conci, and N. Sebe, “Real time detection of social interactions in surveillance video,†in Computer Vision—ECCV 2012. Workshops and Demonstrations, pp. 111–120, Springer, Berlin, Germany, 2012.

      [27] D. Tosato,M. Farenzena, M. Spera, V.Murino, and M. Cristani, “Multi-class classification on Riemannian manifolds for video surveillance,†in Computer Vision—ECCV 2010, vol. 6312 of LectureNotes in Computer Science, pp. 378–391, Springer, Berlin, Germany, 2010. https://doi.org/10.1007/978-3-642-15552-9_28.

  • Downloads

  • How to Cite

    Archana, K., & Bhaskara Reddy, P. (2018). To detect abnormal event at ATM system by using image processing based on IOT technologies. International Journal of Engineering & Technology, 7(3), 1000-1004. https://doi.org/10.14419/ijet.v7i3.11773