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Anomaly Detection via Local Coordinate Factorization and Spatio-Temporal Pyramid

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Book cover Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

Anomaly detection, which aims to discover anomalous events, defined as having a low likelihood of occurrence, from surveillance videos, has attracted increasing interest and is still a challenge in computer vision community. In this paper, we propose an efficient anomaly detection approach which can perform both real-time and multi-scale detection. Our approach can handle the change of background. Specifically, Local Coordinate Factorization is utilized to tell whether a spatio-temporal video volume (STV) belongs to an anomaly, which can effectively detect spatial, temporal and spatio-temporal anomalies. And we employ Spatio-temporal Pyramid (STP) to capture the spatial and temporal continuity of an anomalous event, enabling our approach to handle multi-scale and complicated events. We also propose an online method to update the local coordinates, which makes our approach self-adaptive to background change which typically occurs in real-world setting. We conduct extensive experiments on several publicly available datasets for anomaly detection, and the results show that our approach can outperform state-of-the-art approaches, which verifies the effectiveness of our approach.

This research was supported by National Key Basic Research Project of China (973 Program) 2011CB302400 and National Nature Science Foundation of China (NSFC Grant No. 61071156 and 61131003).

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Notes

  1. 1.

    http://www.cse.yorku.ca/vision/research/.

  2. 2.

    http://www.svcl.ucsd.edu/projects/anomaly.

References

  1. Troscianko, T., Holmes, A., Stillman, J., Mirmehdi, M., Wright, D., Wilson, A.: What happens next? the predictability of natural behaviour viewed through CCTV cameras. Perception 33, 87–101 (2004)

    Article  Google Scholar 

  2. Keval, H., Sasse, M.: not the usual suspects: a study of factors reducing the effectiveness of CCTV. Secur. J. 23, 134–154 (2010)

    Article  Google Scholar 

  3. Haering, N., Venetianer, P., Lipton, A.: The evolution of video surveillance: an overview. Mach. Vis. Appl. 19, 279–290 (2008)

    Article  MATH  Google Scholar 

  4. Brax, C., Niklasson, L., Smedberg, M.: Finding behavioural anomalies in public areas using video surveillance data. In: Proceedings of 11th International Conference on Information Fusion (2008)

    Google Scholar 

  5. Ivanov, I., Dufaux, F., Ha, T., Ebrahimi, T.: Towards generic detection of unusual events in video surveillance. In: Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance (2009)

    Google Scholar 

  6. Li, J., Gong, S., Xiang, T.: Discovering multi-camera behaviour correlations for on-the-fly global activity prediction and anomaly detection. In: Proceedings of IEEE International Conference on Computer Vision Workshops (2009)

    Google Scholar 

  7. Liu, C., Wang, G., Ning, W., Lin, X., Li, L., Liu, Z.: Anomaly detection in surveillance video using motion direction statistics. In: Proceedings of IEEE International Conference on Image Processing (2010)

    Google Scholar 

  8. Loy, C., Xiang, T., Gong, S.: Detecting and discriminating behavioural anomalies. Pattern Recogn. 44, 117–132 (2011)

    Article  MATH  Google Scholar 

  9. Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-supervised adapted HMMs for unusual event detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  10. R. Sillito, R.F.: Semi-supervised learning for anomalous trajectory detection. In: Proceedings of British Machine Vision Conference (2008)

    Google Scholar 

  11. 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, 555–560 (2008)

    Article  Google Scholar 

  12. Kim, J., Grauman, K.: Observe locally, infer globally: a spacectime MRF for detecting abnormal activities with incremental updates. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  13. 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 (2009)

    Google Scholar 

  14. Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  15. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2011)

    Google Scholar 

  16. Bertini, M., Bimbo, A.D., Seidenari, L.: Multi-scale and realtime non-parametric approach for anomaly detection and localization. CVIU 116(3), 320–329 (2012)

    Google Scholar 

  17. Roshtkhari, M.J., Levine, M.D.: Online dominant and anomalous behavior detection in videos. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  18. Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  19. Jiang, F., Wu, Y., Katsaggelos, A.: A dynamic hierarchical clustering method for trajectory-based unusual video event detection. IEEE Trans. Image Process. (TIP) 14, 907–913 (2009)

    Article  MathSciNet  Google Scholar 

  20. Khalid, S.: Activity classification and anomaly detection using m-medoids based modelling of motion patterns. Pattern Recogn. 43, 3636–3647 (2010)

    Article  MATH  Google Scholar 

  21. 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 

  22. Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Advances in Neural Information Processing Systems (2009)

    Google Scholar 

  23. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  24. Morris, B.T., Trivedi, M.M.: Trajectory learning for activity understanding: unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2287–2301 (2011)

    Article  Google Scholar 

  25. Ouivirach, K., Gharti, S., Dailey, M.N.: Incremental behavior modeling and suspicious activity detection. Pattern Recogn. 46, 671–680 (2013)

    Article  Google Scholar 

  26. Benezeth, Y., Jodoin, P.M., Saligrama, V.: Abnormality detection using low-level co-occurring events. Pattern Recogn. Lett. 32, 423–431 (2011)

    Article  Google Scholar 

  27. Hospedales, T., Gong, S., Xiang, T.: Video behaviour mining using a dynamic topic model. Int. J. Comput. Vis. 98, 303–323 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  28. Benezeth, Y., Jodoin, P.M., Saligrama, V., Rosenberger, C.: Abnormal events detection based on spatio-temporal co-occurences. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  29. Ermis, E.B., Saligrama, V., Jodoin, P.M., Konrad, J.: Motion segmentation and abnormal behavior detection via behavior clustering. In: Proceedings of IEEE International Conference on Image Processing (2008)

    Google Scholar 

  30. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foregroundbackground segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)

    Article  Google Scholar 

  31. Mittal, A., Monnet, A., Paragios, N.: Scene modeling and change detection in dynamic scenes: a subspace approach. CVIU 113, 63–79 (2009)

    Google Scholar 

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

    Article  Google Scholar 

  33. Zhu, X., Liu, Z.: Human behavior clustering for anomaly detection. Front. Comput. Sci. Chin. 5(3), 279–289 (2011)

    Article  Google Scholar 

  34. Blei, D., NG, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  35. Hospedales, T.M., Jian, L., Shaogang, G., Tao, X.: Identifying rare and subtle behaviors: a weakly supervised joint topic model. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2451–2464 (2011)

    Article  Google Scholar 

  36. Li, J., Gong, S., Xiang, T.: Learning behavioural context. Int. J. Comput. Vis. 97(3), 276–304 (2012)

    Article  MATH  Google Scholar 

  37. Ricci, E., Zen, G., Sebe, N., Messelodi, S.: A prototype learning framework using EMD: application to complex scenes analysis. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2012)

    Google Scholar 

  38. Roshtkhari, M.J., Levine, M.D.: A multi-scale hierarchical codebook method for human action recognition in videos using a single example. In: Conference on Computer and Robot Vision (2012)

    Google Scholar 

  39. Hospedales, T.M., Jian, L., Shaogang, G., Tao, X.: Identifying rare and subtle behaviors: a weakly supervised joint topic model. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2451–2464 (2012)

    Article  Google Scholar 

  40. P. Jodoin, J.K., Saligrama, V.: Modeling background activity for behavior subtraction. In: International Conference on Distributed Smart Cameras (2008)

    Google Scholar 

  41. Jodoin, P., Saligrama, V., Konrad, J.: Behavior subtraction. IEEE Trans. Image Process. (TIP) 21(9), 4244–4255 (2012)

    Article  MathSciNet  Google Scholar 

  42. Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: International Conference on Multimedia (2007)

    Google Scholar 

  43. Nesterov, Y.: Gradient methods for minimizing composite objective function. In: CORE (2007)

    Google Scholar 

  44. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  45. Lee, D.D., Seung, H.S.: Learning the parts of objects by nonnegative matrix factorization. Nature 401(6755), 788–791 (1999)

    Article  Google Scholar 

  46. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems (2001)

    Google Scholar 

  47. Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548–1560 (2011)

    Article  Google Scholar 

  48. Lin, C.J.: On the convergence of multiplicative update algorithms for non-negative matrix factorization. IEEE Trans. Neural Netw. 18(6), 1589–1596 (2007)

    Article  Google Scholar 

  49. Zaharescu, A., Wildes, R.: Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 563–576. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  50. Saligrama, V., Zhu, C.: Video anomaly detection based on local statistical aggregates. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  51. Reddy, V., Sanderson, C., Lovell, B.C.: Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception, Peking University, Beijing, P.R. China

    Tan Xiao, Chao Zhang, Hongbin Zha & Fangyun Wei

  2. CRSC Communication & Information Corporation, Beijing, P.R. China

    Tan Xiao

Authors
  1. Tan Xiao
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  2. Chao Zhang
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  3. Hongbin Zha
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  4. Fangyun Wei
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Corresponding author

Correspondence to Tan Xiao .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Xiao, T., Zhang, C., Zha, H., Wei, F. (2015). Anomaly Detection via Local Coordinate Factorization and Spatio-Temporal Pyramid. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_5

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  • Publisher Name: Springer, Cham

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