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Improving Usability and Intrusion Detection Alerts in a Home Video Surveillance System

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Computer Science – CACIC 2020 (CACIC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1409))

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Abstract

The purpose of this work is improving the functionality and usability of a low cost commercial surveillance system. The original system provides simple motion detection and sends alert messages by means of FTP or email. The modified system adds a software layer to the original system for implementing desirable image processing features. Particularly, people detection functionality was implemented by means of Oriented Gradient Histograms. The modified system also adds the use of Telegram messaging service for sending alerts. When the camera detects motion, the modified system improves the alert information with the results of the intruder detection algorithm. System Usability Scale (SUS) was used to compare the usability of both systems and the results showed that the modified system improved the original one in terms of usability.

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Notes

  1. 1.

    https://www.tp-link.es/products/details/cat-19_NC220.html.

  2. 2.

    https://www.tplinkcloud.com/.

  3. 3.

    https://www.djangoproject.com.

  4. 4.

    https://github.com/seba3c/scamera.

References

  1. Dong, E., Zhang, Y., Du, S.: An automatic object detection and tracking method based on video surveillance. In: 2020 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1140–1144. IEEE (2020)

    Google Scholar 

  2. Jamiya, S.S., Rani, E.: A survey on vehicle detection and tracking algorithms in real time video surveillance. Int. J. Sci. Technol. Res. 8(10) (2019). ISSN 2277-8616

    Google Scholar 

  3. Ennehar, B.C.: New face features to detect multiple faces in complex background. Evol. Syst. 10(2), 79–95 (2019). https://doi.org/10.1007/s12530-017-9211-y

    Article  Google Scholar 

  4. Alkanhal, L., et al.: Super-resolution using deep learning to support person identification in surveillance video (IJACSA). Int. J. Adv. Comput. Sci. Appl. 11(7) (2020)

    Google Scholar 

  5. Ijjina, E.P., Kanahasabai, G., Joshi, A.S.: Deep learning based approach to detect customer age, gender and expression in surveillance video. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6. IEEE (2020)

    Google Scholar 

  6. Alturki, A.S., Ibrahim, A.H.: Real time action recognition in surveillance video using machine learning. Int. J. Eng. Res. Technol. 13(8), 1874–1879 (2020). ISSN 0974-3154

    Google Scholar 

  7. Rashmi, M., Ashwin, T.S., Guddeti, R.M.R.: Surveillance video analysis for student action recognition and localization inside computer laboratories of a smart campus. Multimed. Tools Appl. 80(2), 2907–2929 (2021). https://doi.org/10.1007/s11042-020-09741-5

    Article  Google Scholar 

  8. Zitouni, M., Śluzek, A.: Video-surveillance tools for monitoring social responsibility under covid-19 restrictions. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds.) ICCVG 2020. LNCS, vol. 12334, pp. 227–239. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59006-2_20

    Chapter  Google Scholar 

  9. Zai-Ying, W., Liu, C.: Design of mobile phone video surveillance system for home security based on embedded system. In: The 27th Chinese Control and Decision Conference (2015 CCDC), pp. 5856–5859. IEEE (2015)

    Google Scholar 

  10. Shete, V., et al.: Intelligent embedded video monitoring system for home surveillance. In: 2016 International Conference on Inventive Computation Technologies (ICICT), pp. 1–4 IEEE (2016)

    Google Scholar 

  11. Castañeda, C.S., Abásolo, G.M.J.: Improving a low cost surveillance system. In: XXVI Congreso Argentino de Ciencias de la Computación. Red UNCI, pp. 777–786 (2020). ISBN 978-987-4417-90-9

    Google Scholar 

  12. Shirbhate, R.S., Mishra, N.D., Pande, R.: Video surveillance system using motion detection: a survey. Adv. Network. Appl. 3(5), 19 (2012)

    Google Scholar 

  13. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK, 26 de agosto (2004)

    Google Scholar 

  14. Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)

    Article  Google Scholar 

  15. Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for real time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S. (eds.) Video Based Surveillance Systems, pp. 135–144. Springer, Boston (2001). https://doi.org/10.1007/978-1-4615-0913-4_11

    Chapter  Google Scholar 

  16. Sobral, A., Vacavant, A.: A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput. Vis. Image Underst. 122, 4–21 (2014)

    Article  Google Scholar 

  17. Dou, J., Qin, Q., Tu, Z.: Background subtraction based on circulant matrix. SIViP 11(3), 407–414 (2016). https://doi.org/10.1007/s11760-016-0975-5

    Article  Google Scholar 

  18. Brutzer, S., Hoferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1937–1944 (2011)

    Google Scholar 

  19. Singla, N.: Motion detection based on frame difference method. Int. J. Inf. Comput. Technol. 4(15), 1559–1565 (2014)

    Google Scholar 

  20. Sengar, S.S., Mukhopadhyay, S.: A novel method for moving object detection based on block based frame differencing. In: 3rd International Conference on Recent Advances in Information Technology, pp. 462–472 (2016)

    Google Scholar 

  21. Fei, M., Li, J., Liu, H.: Visual tracking based on improved foreground detection and perceptual hashing. Neurocomputing. 152(C), 413–428 (2015)

    Article  Google Scholar 

  22. Molchanov, V.V., Vishnyakov, B.V., Vizilter, Y.V., Vishnyakova, O.V., Knyaz, V.A.: Pedestrian detection in video surveillance using fully convolutional YOLO neural network. In: Proceedings. SPIE 10334, Automated Visual Inspection and Machine Vision II, p. 103340Q (2017)

    Google Scholar 

  23. Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance, In: Proceedings Ninth IEEE International Conference on Computer Vision, Nice, France (2005)

    Google Scholar 

  24. Hamdoun, O., Moutarde, F.: Keypoints-based background model and foreground pedestrian extraction for future smart cameras. In: 3rd ACM/IEEE International Conference on Distributed Smart Cameras, Como, Italy (2009)

    Google Scholar 

  25. Angelova, A., Krizhevsky, A., Vanhoucke, V., Ogale, A., Ferguson, D.: Real-time pedestrian detection with deep network cascades. In: Proceedings of BMVC (2015)

    Google Scholar 

  26. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA (2005)

    Google Scholar 

  27. Rosebrock: Histogram of oriented gradients and object detection. https://www.pyimagesearch.com/2014/11/10/histogram-oriented-gradients-object-detection. Accessed 12 Mar 2021

  28. Rosebrock: Pedestrian Detection OpenCV. https://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv. Accessed 12 Mar 2021

  29. ISO: Ergonomic requirements for office work with visual display terminals, 9241-11. ISO, Marzo (1998)

    Google Scholar 

  30. Bangor, A., Kortum, P.T., Miller, J.T.: An empirical evaluation of the system usability scale. Int. J. Hum Comput Interact. 24(6), 574–594 (2008)

    Article  Google Scholar 

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Correspondence to María José Abásolo .

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Abásolo, M.J., Castañeda, C.S. (2021). Improving Usability and Intrusion Detection Alerts in a Home Video Surveillance System. In: Pesado, P., Eterovic, J. (eds) Computer Science – CACIC 2020. CACIC 2020. Communications in Computer and Information Science, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-75836-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-75836-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75835-6

  • Online ISBN: 978-3-030-75836-3

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