Skip to main content
Log in

A modular system for global and local abnormal event detection and categorization in videos

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper presents a modular system for both abnormal event detection and categorization in videos. Complementary normalcy models are built both globally at the image level and locally within pixels blocks. Three features are analyzed: (1) spatio-temporal evolution of binary motion where foreground pixels are detected using an enhanced background subtraction method that keeps track of temporarily static pixels; (2) optical flow, using a robust pyramidal KLT technique; and (3) motion temporal derivatives. At the local level, a normalcy MOG model is built for each block and for each flow feature and is made more compact using PCA. Then, the activity is analyzed qualitatively using a set of compact hybrid histograms embedding both optical flow orientation (or temporal gradient orientation) and foreground statistics. A compact binary signature of maximal size 13 bits is extracted from these different features for event characterization. The performance of the system is illustrated on different datasets of videos recorded on static cameras. The experiments show that the anomalies are well detected even if the method is not dedicated to one of the addressed scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Notes

  1. Single instruction multiple data.

  2. http://openmp.org/.

  3. Unusual Crowd Activity Dataset: http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.

  4. http://homepages.inf.ed.ac.uk/rbf/BEHAVE/.

  5. Surveillance imProved sYstem https://itea3.org/project/spy.html.

References

  1. Xiang, T., Gong, S.: Video behavior profiling for anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 893–908 (2008)

    Article  Google Scholar 

  2. Antic, B., Ommer, B.: Video parsing for abnormality detection. In: International conference on computer vision (ICCV), pp. 2415–2422 (2011)

  3. Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36, 18–32 (2014)

    Article  Google Scholar 

  4. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)

    Article  Google Scholar 

  5. Tabia, H., Gouiffès, M., Lacassagne, L.: Motion modeling for abnormal event detection in crowd scenes. In: International symposium on signal, Images, Video and communications, University of Valenciennes, France (2012)

  6. Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intelli. 22(8), 747–757 (2000)

    Article  Google Scholar 

  7. Zhang, T., Lu, H., Li, S.: Learning semantic scene models by object classification and trajectory clustering. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 1940–1947 (2009)

  8. Siebel, N.T., Maybank, S.J.: Fusion of multiple tracking algorithms for robust people tracking. In: European conference on computer vision-Part IV (ECCV). Springer, London, pp. 373–387 (2002)

  9. Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: IEEE conference on computer vision and pattern recognition, CVPR, 2008, pp. 1–8 (2008)

  10. Jiang, F., Yuan, J., Tsaftaris, S.A., Katsaggelos, A.K.: Anomalous video event detection using spatiotemporal context. Comput. Vis. Image Underst. 115(3), 323–333 (2011)

    Article  Google Scholar 

  11. Wu, S., Moore, B.E., Shah, M.: ‘Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 2054–2060 (2010)

  12. Benezeth, Y., Jodoin, P.-M., Saligrama, V., Rosenberger, C.: Abnormal events detection based on spatio-temporal co-occurences. In: IEEE conference on computer vision and pattern recognition, CVPR, 2009, pp. 2458–2465 (2009)

  13. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. IEEE conference on computer vision and pattern recognition (CVPR) 2011, 3449–3456 (2011)

    Google Scholar 

  14. Zhao, B., Fei-Fei, L., Xing, E.: Online detection of unusual events in videos via dynamic sparse coding. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011, pp. 3313–3320 (2011)

  15. Kim, J., Grauman, K.: Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental updates. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 2921–2928 (2009)

  16. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition, CVPR, 2009, pp. 935–942 (2009)

  17. Cui, X., Liu, Q., Gao, M., Metaxas, D.: Abnormal detection using interaction energy potentials. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 3161–3167 (2011)

  18. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E. 51(5), 4282–4286 (1995)

    Article  Google Scholar 

  19. Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE conference on computer vision and pattern recognition, CVPR, 2009, pp. 1446–1453 (2009)

  20. Boiman, O., Irani, M.: Detecting irregularities in images and in video. Int. J. Comput. Vis. 74(1), 17–31 (2007)

    Article  Google Scholar 

  21. Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 2112–2119 (2012)

  22. Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: IEEE conference on computer vision and pattern recognition (CVPR), 2010, pp. 1975–1981 (2010)

  23. Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: IEEE international conference on computer vision (ICCV), pp. 2720–2727 (2013)

  24. Hamid, R., Johnson, A., Batta, S., Bobick, A., Isbell, C., Coleman, G.: Detection and explanation of anomalous activities: representing activities as bags of event n-grams. In: IEEE Conference on computer vision and pattern recognition (CVPR), vol. 1, pp. 1031–1038 (2005)

  25. Zhang, D., Sullivan, T., Briciu Burghina, C., Murphy, K., McGuinness, K., O’Connor, N.E., Smeaton, A.F., Regan, F.: Detection and classification of anomalous events in water quality datasets within a smart city-smart bay project Int. J Adv. Intell. Syst. 7(1&2): 167–178 (2014)

  26. Kruegel, C., Mutz, D., Robertson, W., Valeur, F.: Bayesian event classification for intrusion detection. In: Computer security applications conference, IEEE, pp. 14–23 (2003)

  27. Cheung, S.-C.S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Proceedings of SPIE Visual Communications and Image Processing, vol. 5308, pp. 881–892 (2004)

  28. Harville, M.: A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In: European conference on computer vision-Part III (ECCV), Springer, London, pp. 543–560 (2002)

  29. Elgammal, A.M., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: European conference on computer vision-Part II (ECCV). Springer, London, pp. 751–767 (2000)

  30. Boult, T.E., Micheals, R., Gao, X., Lewis, P., Power, C., Yin, W., Erkan, A.: Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets. In: IEEE international workshop on visual surveillance, pp. 48–55 (1999)

  31. Lucas, B.D. and Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International joint conference on artifical intelligence, pp. 674–679 (1981)

  32. Tomasi, C., Kanade, T.: Detection and tracking of point features. Carnegie Mellon University Technical Report, Tech. Rep. CMU-CS-91-132 (1991)

  33. Gouiffès, M., Collewet, C., Fernandez-Maloigne, C., Trémeau, A.: A study on local photometric models and their application to robust tracking. Comput. Vis. Image Underst. 116, 896–907 (2012)

    Article  Google Scholar 

  34. Bouguet, J.-Y.: Pyramidal implementation of the lucas kanade feature tracker. Bouguet, J.Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corp. 5(1–10):4 (2001)

  35. Gouiffès, M., Planes, B., Jacquemin, C.: Htri: high time range imaging. J. Vis. Commun. Image Represent. 24(3), 361–372 (2013)

    Article  Google Scholar 

  36. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  37. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 559–572 (1901)

    Article  MATH  Google Scholar 

  38. Defays, D.: An efficient algorithm for a complete link method. Comput. J. 20(4), 364–366 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  39. Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: European workshop on advanced video based surveillance systems, vol. 5308 (2001)

  40. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 555–560 (2008)

  41. Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 2790–2797 (2009)

Download references

Acknowledgments

This research is supported by the European Project ITEA2 SPY Surveillance imProved sYstem https://itea3.org/project/spy.html.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michèle Gouiffès.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdallah, A.C.B., Gouiffès, M. & Lacassagne, L. A modular system for global and local abnormal event detection and categorization in videos. Machine Vision and Applications 27, 463–481 (2016). https://doi.org/10.1007/s00138-016-0752-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-016-0752-z

Keywords

Navigation