Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1115–1123 | Cite as

Abnormal event detection in crowded scenes using one-class SVM

  • Somaieh Amraee
  • Abbas VafaeiEmail author
  • Kamal Jamshidi
  • Peyman Adibi
Original Paper


In this paper, a new method for detecting abnormal events in public surveillance systems is proposed. In the first step of the proposed method, candidate regions are extracted, and the redundant information is eliminated. To describe appearance and motion of the extracted regions, HOG-LBP and HOF are calculated for each region. Finally, abnormal events are detected using two distinct one-class SVM models. To achieve more accurate anomaly localization, the large regions are divided into non-overlapping cells, and the abnormality of each cell is examined separately. Experimental results show that the proposed method outperforms existing methods based on the UCSD anomaly detection video datasets.


Anomaly detection Crowded scenes One-class SVM Optical flow 


  1. 1.
    Sodemann, A., Ross, M., Borghetti, B.: A review of anomaly detection in automated surveillance. IEEE Trans. Syst. Man Cybern. 42(6), 1257–1272 (2012)CrossRefGoogle Scholar
  2. 2.
    Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis Comput. 29(10), 983–1009 (2013)CrossRefGoogle Scholar
  3. 3.
    Feng, W., Liu, R., Zhu, M.: Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera. Signal Image Video Process 8(6), 1129–1138 (2014)CrossRefGoogle Scholar
  4. 4.
    Zhou, S.H., et al.: Unusual event detection in crowded scenes by trajectory analysis. In: Proceedings of ICASSP, pp. 1300–1304 (2015)Google Scholar
  5. 5.
    Kumar, D., et al.: A visual-numeric approach to clustering and anomaly detection for trajectory data. Vis Comput. 33(3), 265–281 (2017)CrossRefGoogle Scholar
  6. 6.
    Junejo, I.: Using dynamic Bayesian network for scene modeling and anomaly detection. Signal Image Video Process. 4(1), 1–10 (2010)CrossRefzbMATHGoogle Scholar
  7. 7.
    Rao, Y.: Automatic vehicle recognition in multiple cameras for video surveillance. Vis. Comput. 31(3), 271–280 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhang, C., Chen, W., et al.: A multiple instance learning and relevance feedback framework for retrieving abnormal incidents in surveillance videos. J. Multimed. 5(4), 310–321 (2010)Google Scholar
  9. 9.
    Vallejo, D., Albusac, J., Jimenez, L.: A cognitive surveillance system for detecting incorrect traffic behaviors. Expert Syst. Appl. 36(7), 10503–10511 (2009)CrossRefGoogle Scholar
  10. 10.
    Albusac, J., et al.: Intelligent surveillance based on normality analysis to detect abnormal behaviors. Pattern Recognit. Artif. Intell. 23(7), 1223–1244 (2009)CrossRefGoogle Scholar
  11. 11.
    Varadarajan, J., Odobez, J.: Topic models for scene analysis and abnormality detection. In: Proceedings of IEEE Conference on Computer Vision Workshops, pp. 1338–1345 (2009)Google Scholar
  12. 12.
    Tang, S., Andriluka, M., Schiele, B.: Detection and tracking of occluded people. Int. J. Comput. Vis. 110(1), 58–69 (2014)CrossRefGoogle Scholar
  13. 13.
    Roshtkhari, M., Levine, D.: A non-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Comput. Vis. Image Underst. 117(10), 1436–1452 (2013)CrossRefGoogle Scholar
  14. 14.
    Reddy, V., Sanderson, C., Lovell, B.: Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 55–61 (2011)Google Scholar
  15. 15.
    Mahadevan, V., Li, W., et al.: Anomaly detection in crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1975–1981 (2010)Google Scholar
  16. 16.
    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, pp. 2921–2928 (2009)Google Scholar
  17. 17.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 935–942 (2009)Google Scholar
  18. 18.
    Zhang, T., et al.: A new method for violence detection in surveillance scenes. Multimed. Tools Appl. 75(12), 7327–7349 (2016)CrossRefGoogle Scholar
  19. 19.
    Ren, W., et al.: Unsupervised kernel learning for abnormal events detection. Vis. Comput. 31(3), 245–255 (2015)CrossRefGoogle Scholar
  20. 20.
    Zhou, S.H., et al.: Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Proc. Image Comm. 47, 358–368 (2016)CrossRefGoogle Scholar
  21. 21.
    Yu, Y., Shen, W., Huang, H., Zhang, Zh: Abnormal event detection in crowded scenes using two sparse dictionaries with saliency. J. Electron. Imaging 26(3), 33013 (2017)CrossRefGoogle Scholar
  22. 22.
    Biswas, S., Babu, R.V.: Anomaly detection in compressed H.264/AVC video. Multimed. Tools Appl. 74(24), 11099–11115 (2015)CrossRefGoogle Scholar
  23. 23.
    Zaharescu, A., Wildes, R.: Anomalous behavior detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing. In: Proceedings of European Conference on Computer Vision, pp. 563–576 (2010)Google Scholar
  24. 24.
    Bertini, M., Bimbo, A., Seidenari, L.: Multi-scale and real-time nonparametric approach for anomaly detection and localization. Comput. Vis. Image Underst. 116(3), 320–329 (2012)CrossRefGoogle Scholar
  25. 25.
    Li, T., Chang, H., et al.: Crowded scene analysis: a survey. IEEE Trans. Circuits Syst. Video Technol. 25(3), 367–386 (2015)CrossRefGoogle Scholar
  26. 26.
    Amraee, S., et al.: Anomaly detection and localization in crowded scenes using connected component analysis. Multimed. Tools Appl. (2017)
  27. 27.
    Kangwei, L., et al.: Abnormal event detection and localization using level set based on hybrid features. Signal Image Video Process. (2017)
  28. 28.
    Leyva, R., et al.: Abnormal event detection in videos using binary features. In: International Conference on Telecommunications and Signal Processing (TSP) (2017)Google Scholar
  29. 29.
    Sabokrou, M., et al.: Real-time anomaly detection and localization in crowded scenes. In: IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 320–329 (2015)Google Scholar
  30. 30.
    Sabokrou, M., et al.: Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder. Electron. Lett. 52(13), 1122–1124 (2016)CrossRefGoogle Scholar
  31. 31.
    Lee, D., et al.: Motion influence map for unusual human activity. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1612–1623 (2015)CrossRefGoogle Scholar
  32. 32.
    Cong, Y., Yuan, J., Yandong, T.: Video anomaly search in crowded scenes via spatio-temporal motion context. IEEE Trans. Inf. Forensics Secur. 8(10), 1590–1599 (2013)CrossRefGoogle Scholar
  33. 33.
    Revathi, A., Kumar, D.: An efficient system for anomaly detection using deep learning classifier. Signal Image Video Process. 11(2), 291–299 (2017)CrossRefGoogle Scholar
  34. 34.
    Xiang, T., Gong, Sh: Video behavior profiling for anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 893–908 (2008)CrossRefGoogle Scholar
  35. 35.
    Cheng, W., Chen, T., Fang, H.: Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. IEEE Trans. Image Process. 24(12), 5288–5301 (2015)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Xu, D., Yan, Y., Ricci, E., Sebe, N.: Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput. Vis. Image Underst. 156(C), 117–127 (2017)CrossRefGoogle Scholar
  37. 37.
    Miao, Y., Song, J.: Abnormal event detection based on SVM in video surveillance. In: Proceedings of IEEE Workshop on Advanced Research and Technology in Industry Applications, pp. 1379–1383 (2014)Google Scholar
  38. 38.
    Chen, Y., Qian, J., Saligrama, V.: A new one-class SVM for anomaly detection. In: Proceedings of IEEE ICASSP, pp. 3567–3571 (2013)Google Scholar
  39. 39.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  40. 40.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multi resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  41. 41.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Proceedings of European Conference on Computer Vision, pp. 428–441 (2006)Google Scholar
  42. 42.
    Barron, L., Fleet, J., Beauchemin, S., Burkitt, A.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)CrossRefGoogle Scholar
  43. 43.
    Schölkopf, B., et al.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)CrossRefzbMATHGoogle Scholar
  44. 44.
    UCSD Anomaly Detection Dataset.:

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer EngineeringUniversity of IsfahanIsfahanIran

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