Suspicious and Violent Activity Detection of Humans Using HOG Features and SVM Classifier in Surveillance Videos

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 730)

Abstract

Crimes such as theft, violence against people, damage to property, etc., have become quite common in a society, which a serious concern. The traditional surveillance systems act like post mortem tools in the sense that they can be used for the investigation to detect the person behind the theft, but it is only after the crime has already occurred. In this chapter, we propose a method for automatically detecting the suspicious or violent activities of a person from the surveillance video. We train the SVM classifier with the HOG features extracted from the video frames of two types: frames showing no violent activities and those showing violent activities like kicking, pushing, punching, etc. In the testing phase, the frames from the surveillance video are read and processed in order to classify them as violent or normal frames. If the frames classified as violent frames are detected, an alarm is raised to alert the controller. It can be used to keep track of the time duration for which a person is found loitering at a place being monitored. If the time exceeds a predefined threshold, the alarm is raised to alert about any potential suspicious activity so that it can be checked on time.

References

  1. 1.
    Spasic, N.: Anomaly Detection and Prediction of Human Actions in a Video Surveillance Environment, Master Thesis, Computer Science Dept., Cape Town University, South Africa, December (2007)Google Scholar
  2. 2.
    Haque, M., Murshed, M.: Robust background subtraction based on perceptual mixture of gaussians with dynamic adaptation speed. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW 2012), Melbourne, Australia, pp. 396–401, July 2012Google Scholar
  3. 3.
    Regazzoni, C., Cavallaro, A., Wu, Y., Konrad, J., Hampapur, A.: Video analytics for surveillance: theory and practice. IEEE Signal Process. Mag. 27(5), 16–17 (2010)CrossRefGoogle Scholar
  4. 4.
    Reddy, V., Sanderson, C., Sanin, A., Lovell, B.C.: MRF-based background initialisation for improved foreground detection in cluttered surveillance videos. In: 10th Asian Conference on Computer Vision (ACCV 2010), Queenstown, New Zealand, Vol. Part III, pp. 547–559, November 2010Google Scholar
  5. 5.
    Liu, L., Tao, W., Liu, J., Tian, J.: A variational model and graph cuts optimization for interactive foreground extraction. Signal Process. J. 91(5) (2011)Google Scholar
  6. 6.
    Suau, X., Casas, J.R., Ruiz-Hidalgo, J.: Multi-resolution illumination compensation for foreground extraction. In: 16th IEEE International Conference on Image Processing (ICIP 2009), pp. 3189–3192, November 2009Google Scholar
  7. 7.
    Loy, C.C.: Activity understanding and unusual event detection in surveillance videos, Ph.D. dissertation, Queen Mary University of London (2010)Google Scholar
  8. 8.
    Cavallaro, A., Steiger, O., Ebrahimi, T.: Tracking video objects in cluttered background. IEEE Trans. Circuits Syst. Video Technol. 15(4), 575–584 (2005)CrossRefGoogle Scholar
  9. 9.
    Karasulu, B.: Review and evaluation of wellknown methods for moving object detection and tracking in videos. J. Aeronaut. Space Technol. 4(4), 11–22 (2010)Google Scholar
  10. 10.
    Cilla, R., Patricio, M.A., Berlanga, A., Molina, J.M.: Human action recognition with sparse classification and multiple-view learning. Expert Syst. J. (2013). Wiley Publishing LtdGoogle Scholar
  11. 11.
    Javed, O., Shah, M.: Tracking and object classification for automated surveillance. In: Proceedings of the 7th European Conference on Computer Vision, Part-IV, pp. 343–357 (2002)Google Scholar
  12. 12.
    Li, T., Chang, H., Wang, M., Ni, B., Hong, R., Yan, S.: Crowded scene analysis: a survey. IEEE Trans. Circuits Syst. Video Technol. 25(3), 367–386 (2015)CrossRefGoogle Scholar
  13. 13.
    Patino, L., Ferryman, J., Beleznai, C.: Abnormal behaviour detection on queue analysis from stereo cameras. In: Advanced Video and Signal Based Surveillance (AVSS), 12th IEEE International Conference, pp. 1–6, August 2015Google Scholar
  14. 14.
    Chan, A.B., Morrow, M., Vasconcelos, N.: Analysis of crowded scenes using holistic properties. In: Performance Evaluation of Tracking and Surveillance workshop at CVPR (2009)Google Scholar
  15. 15.
    Lee, L., Romano, R., Stein, G.: Introduction to the special section on video surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 8, 740–745 (2000)Google Scholar
  16. 16.
    Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19(5–6), 345–357 (2008)CrossRefGoogle Scholar
  17. 17.
    Baumann, A., Boltz, M., Ebling, J., Koenig, M., Loos, H., Merkel, M., Niem, W., Warzelhan, J., Yu, J.: A review and comparison of measures for automatic video surveillance systems. EURASIP J. Image Video Process., 1–30 (2008)Google Scholar
  18. 18.
    Cai, Y., de Freitas, N., Little, J.J.: Robust visual tracking for multiple targets. In: European Conference on Computer Vision, LNCS, Vol. 3954, pp. 107–118 (2006)Google Scholar
  19. 19.
    Chang, T., Gong, S., Ong, E.: Tracking multiple people under occlusion using multiple cameras. In: British Machine Vision Conference, pp. 566–575 (2000)Google Scholar
  20. 20.
    Donalek, C.: Supervised and Unsupervised learning (2011)Google Scholar
  21. 21.
    Benezeth, Y., Jodoin, P.M., Saligrama, V.: Abnormality detection using low-level co-occurring events. Pattern Recogn. Lett. 32(3), 423–431 (2011)CrossRefGoogle Scholar
  22. 22.
    Isupova, O., Kuzin, D., Mihaylova, L.: Abnormal behaviour detection in video using topic modeling. In: USES Conference Proceedings. The University of Sheffield, June 2015Google Scholar
  23. 23.
    Cosar, S., Donatiello, G., Bogorny, V., Garate, C., Alvares, L.O., Bremond, F.: Towards abnormal trajectory and event detection in video surveillanceGoogle Scholar
  24. 24.
  25. 25.
    How a Kalman filter works, in pictures. http://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/
  26. 26.
  27. 27.
    Background subtraction for detection of moving objects. https://computation.llnl.gov/casc/sapphire/background/background.html
  28. 28.
    Machine Learning, Part I: Supervised and Unsupervised Learning. http://www.aihorizon.com/essays/generalai/supervised_unsupervised_machine_learning.htm
  29. 29.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology (Indian School of Mines)DhanbadIndia

Personalised recommendations