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The Need for Automatic Detection of Uncommon Behaviour in Surveillance Systems: A Short Review

  • Lalesh BheechookEmail author
  • Sunilduth Baichoo
  • Maleika Heenaye-Mamode Khan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)

Abstract

Surveillance system used for monitoring existed since decades. However, these surveillance systems required human intervention for the monitoring of suspicious behaviors. With the rapid revolution in technology, automatic detection of uncommon behaviors is gaining much attention from researchers. Being rapidly accepted in most public places to ensure transparency and security, surveillance systems are contributing in many applications like live traffic monitoring, crime scenes, and old people care. The deployment of automatic detection is very complex since it requires complex algorithms that should accurately detect uncommon behaviors, which is context-sensitive. In addition, it involves a lot of incoming data from various cameras, making it more challenging. In this perspective, the factors affecting surveillance systems and the techniques devised so far to detect uncommon behavior from these systems are analyzed and discussed. Robust automatic detection applications may yield to proactive decisions to be taken to prevent any harms/damages that would be caused by any uncommon/suspicious behaviors. Thus, it is important to explore techniques that can be used to implement automatic surveillance systems.

Keywords

Surveillance Uncommon behavior Automatic detection 

References

  1. 1.
    Morris, B.T., Trivedi, M.M.: A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1114–1127 (2008)CrossRefGoogle Scholar
  2. 2.
    Emonet, R., Varadarajan, J., Odobez, J.M.: Multi-camera open space human activity discovery for anomaly detection. In: IEEE conference on Advanced Video Signal and Surveillance (AVSS), Klagenfurt, Austria (2011)Google Scholar
  3. 3.
    Remagnino, P., Velastin, S.A., Foresti, G.L., Trivedi, M.: Novel concepts and challenges for the next generation of video surveillance systems. Mach. Vis. Appl. 18(3–4), 135–137 (2007)CrossRefGoogle Scholar
  4. 4.
    Isupova, O., Kuzin, D., Mihaylova, L.: Dynamic hierarchical dirichlet process for abnormal behaviour detection in video. In: USES Conference Proceedings. University of Sheffield Sheffield Engineering Symposium, Sheffield, 24 June 2015.  https://doi.org/10.15445/02012015
  5. 5.
    Harrigan, J., Rosenthal, R., Scherer, K.: The New Handbook of Methods in Nonverbal Behavior Research, Oxford University Press (2005)Google Scholar
  6. 6.
    Moeslund, T. B., Hilton, A., Kruger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. Spec. Issue Model. People Vis. based underst. person’s shape, appearance, mov. behav. 104(2–3), 90–126Google Scholar
  7. 7.
    Ibrahim, S.: A comprehensive review on intelligent surveillance systems. Commun. Sci. Technol. 1(1) (2016)Google Scholar
  8. 8.
    Baig, A. R., Jabeen, H.: Big data analytics for behavior monitoring of students. In: Symposium on Data Mining Applications, SDMA, Riyadh, Saudi Arabia. Procedia Computer Science. 1877–1899, 30 March 2016Google Scholar
  9. 9.
    Wells, H., Allard, T., Wilson, P.: Crime and cctv in Australia: understanding the relationship. Center for Applied Psychology and Criminology, Bond University, Australia Tech. Rep. (2006)Google Scholar
  10. 10.
    Janke, A.T., Overbeek, D.L., Kocher, K.E., et al.: Exploring the potential of predictive analytics and big data in emergency care. Ann. Emerg. Med. 67, 227–236 (2016)CrossRefGoogle Scholar
  11. 11.
    Jung, J., Yoon, I., Paik, J.: Object occlusion detection using automatic camera calibration for a wide-area video surveillance system. Sensors 16(7), 982 (2016)CrossRefGoogle Scholar
  12. 12.
    Murthy, V., Aravind, C., Jayasri, K., Mounika, K. and Akhil, T.: An automatic motion detection system for a camera surveillance video. Indian J. Sci. Technol. 9(17) (2016)Google Scholar
  13. 13.
    Pojage, P., Gurjar, A.: Automatic fast moving object detection in video of surveillance system. IARJSET 4(5), 190–195 (2017)CrossRefGoogle Scholar
  14. 14.
    Katal, A., Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and good practices, In: 2013 Sixth International Conference on Contemporary Computing (IC3), IEEE, 404–409 (2013)Google Scholar
  15. 15.
    Ganesh, B.R., Appavu, S.:An intelligent video Surveillance Framework with big data management for Indian road traffic system. Int. J. Comput. Appl. Published by Foundation of Computer Science (FCS), NY, USA. 123(10), 12–19, August 2015Google Scholar
  16. 16.
    Piccardi, M.: Background subtraction techniques: a review. In: Proceedings of IEEE. International Conference on Systems, Man and Cybernetics, The Hague, The Netherlands, Vol. 4, pp. 3099–3104, 10–13 October 2004Google Scholar
  17. 17.
    Lin, W., Sun, M., Poovandran, R., Zhang, Z.: Human activity recognition for video surveillance. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Seattle, WA, USA, 2737–2740, 18–21 May 2008Google Scholar
  18. 18.
    Chaaraoui, A., Padilla-Lopez, J., Ferrandez-Pastor, F., Nieto-Hidalgo, M., Florez-Revuelta, F.: A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context. Sensors 2014, 14, 8895–8925 (2014).  https://doi.org/10.3390/s140508895
  19. 19.
    Roshtkhari, M.J., Levine, M.D.: Online dominant and anomalous behavior detection in videos. IEEE Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  20. 20.
    Kumari, S., Mitra, S.K.: Human action recognition using DFT. In: Proceedings of the third IEEE, National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Hubli, India, pp. 239–242, 15–17 December 2011Google Scholar
  21. 21.
    Verma,G.K.: Facial micro-expression recognition using discrete curvelet transform. 2017 Conference on Information and Communication Technology (2017)Google Scholar
  22. 22.
    Beaugendre, A., Miyano, H., Shidera, E., Goto, S.: Human tracking system for automatic video surveillance with particle filters. IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 152–155 (2010)Google Scholar
  23. 23.
    Hoang, L., Ke, S., Hwang, J., Yoo, J., Choi, K.: Human action recognition based on 3D body modeling from Monocular videos. In: Proceedings of Frontiers of Computer Vision Workshop, Tokyo, Japan, pp. 6–13, 2–4 February 2012Google Scholar
  24. 24.
    Adeleh, F., Rahebeh, N.A.: Recognition and classification of human behavior in intelligent surveillance systems using hidden Markov model. IJIGSP 7(12), 31–38 (2015).  https://doi.org/10.5815/ijigsp.2015.12.05(2015)CrossRefGoogle Scholar
  25. 25.
    Moeslund, T., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81, 231–268 (2015)CrossRefGoogle Scholar
  26. 26.
    Agarwal, A., Triggs, B.: Recovering 3D human pose from monocular images. IEEE Trans. Pattern Anal. Mach. Intell. 28, 44–58 (2006)CrossRefGoogle Scholar
  27. 27.
    Arsic, D., Schuller, B., Rigoll, G.: Suspicious behavior detection in public transport by fusion of low-level video descriptors. In: Proceedings of the 8th IEEE International Conference on Multimedia and Expo. IEEE Computer Society Press, Beijing, China, 218–221, (2007)Google Scholar
  28. 28.
    Duque, D., Santos, H., Cortez, P.: Prediction of abnormal behaviors for intelligent video surveillance systems. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (2007)Google Scholar
  29. 29.
    Madden, S.: From databases to big data. IEEE, Internet Comput. 16(3), 4–6 (2012).  https://doi.org/10.1109/mic.2012.50
  30. 30.
    Russom, B.P.:Big data analytics. Tdwi Best Practices Rep. (2011)Google Scholar
  31. 31.
    Tiejun, H.:Surveillance Video: The biggest big data. Computing Now, 7(2) (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lalesh Bheechook
    • 1
    Email author
  • Sunilduth Baichoo
    • 1
  • Maleika Heenaye-Mamode Khan
    • 1
  1. 1.Department of Software and Information SystemsUniversity of MauritiusMokaMauritius

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