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Abnormal event detection in crowded scenes using one-class SVM

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Abstract

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.

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Notes

  1. Individual ROC curves for motion/appearance anomaly detection are only available for Ped2 in [26]. Such curves have not been reported in the other methods.

References

  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)

    Article  Google Scholar 

  2. Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis Comput. 29(10), 983–1009 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Zhou, S.H., et al.: Unusual event detection in crowded scenes by trajectory analysis. In: Proceedings of ICASSP, pp. 1300–1304 (2015)

  5. Kumar, D., et al.: A visual-numeric approach to clustering and anomaly detection for trajectory data. Vis Comput. 33(3), 265–281 (2017)

    Article  Google Scholar 

  6. Junejo, I.: Using dynamic Bayesian network for scene modeling and anomaly detection. Signal Image Video Process. 4(1), 1–10 (2010)

    Article  MATH  Google Scholar 

  7. Rao, Y.: Automatic vehicle recognition in multiple cameras for video surveillance. Vis. Comput. 31(3), 271–280 (2015)

    Article  Google Scholar 

  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. Vallejo, D., Albusac, J., Jimenez, L.: A cognitive surveillance system for detecting incorrect traffic behaviors. Expert Syst. Appl. 36(7), 10503–10511 (2009)

    Article  Google Scholar 

  10. Albusac, J., et al.: Intelligent surveillance based on normality analysis to detect abnormal behaviors. Pattern Recognit. Artif. Intell. 23(7), 1223–1244 (2009)

    Article  Google Scholar 

  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)

  12. Tang, S., Andriluka, M., Schiele, B.: Detection and tracking of occluded people. Int. J. Comput. Vis. 110(1), 58–69 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  18. Zhang, T., et al.: A new method for violence detection in surveillance scenes. Multimed. Tools Appl. 75(12), 7327–7349 (2016)

    Article  Google Scholar 

  19. Ren, W., et al.: Unsupervised kernel learning for abnormal events detection. Vis. Comput. 31(3), 245–255 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  22. Biswas, S., Babu, R.V.: Anomaly detection in compressed H.264/AVC video. Multimed. Tools Appl. 74(24), 11099–11115 (2015)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  25. Li, T., Chang, H., et al.: Crowded scene analysis: a survey. IEEE Trans. Circuits Syst. Video Technol. 25(3), 367–386 (2015)

    Article  Google Scholar 

  26. Amraee, S., et al.: Anomaly detection and localization in crowded scenes using connected component analysis. Multimed. Tools Appl. https://doi.org/10.1007/s11042-017-5061-7 (2017)

  27. Kangwei, L., et al.: Abnormal event detection and localization using level set based on hybrid features. Signal Image Video Process. https://doi.org/10.1007/s11760-017-1153-0 (2017)

  28. Leyva, R., et al.: Abnormal event detection in videos using binary features. In: International Conference on Telecommunications and Signal Processing (TSP) (2017)

  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)

  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)

    Article  Google Scholar 

  31. Lee, D., et al.: Motion influence map for unusual human activity. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1612–1623 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  33. Revathi, A., Kumar, D.: An efficient system for anomaly detection using deep learning classifier. Signal Image Video Process. 11(2), 291–299 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  38. Chen, Y., Qian, J., Saligrama, V.: A new one-class SVM for anomaly detection. In: Proceedings of IEEE ICASSP, pp. 3567–3571 (2013)

  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)

  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)

    Article  MATH  Google Scholar 

  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)

  42. Barron, L., Fleet, J., Beauchemin, S., Burkitt, A.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)

    Article  Google Scholar 

  43. Schölkopf, B., et al.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)

    Article  MATH  Google Scholar 

  44. UCSD Anomaly Detection Dataset.: http://www.svcl.ucsd.edu/projects/anomaly/dataset

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Authors and Affiliations

  1. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Somaieh Amraee, Abbas Vafaei, Kamal Jamshidi & Peyman Adibi

Authors
  1. Somaieh Amraee
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  2. Abbas Vafaei
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  3. Kamal Jamshidi
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  4. Peyman Adibi
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Correspondence to Abbas Vafaei.

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Amraee, S., Vafaei, A., Jamshidi, K. et al. Abnormal event detection in crowded scenes using one-class SVM. SIViP 12, 1115–1123 (2018). https://doi.org/10.1007/s11760-018-1267-z

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  • DOI: https://doi.org/10.1007/s11760-018-1267-z

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