Anomaly Detection in Crowded Scenarios Using Local and Global Gaussian Mixture Models

  • Adrián ToméEmail author
  • Luis Salgado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10617)


This paper presents an objective comparison between two approaches for anomaly detection in surveillance scenarios. Gaussian mixture models (GMM) are used in both cases: globally, with a unique model that covers the whole scene; and locally, with one model per spatial location. The two approaches follow a “bottom-up” approach that avoids any object tracking and motion features extracted with a robust optical flow method. Furthermore, we evaluate the contribution of each feature through a statistical tool called Correlation Feature Selection in order to assure the best performance. Evaluation is done in UCSD dataset, concluding that the global model offers better results, outperforming similar anomaly detection approaches.


Anomaly detection Gaussian mixture model Robust optical flow Correlation feature selection 



This work has been partially supported by the Ministerio de Economa, Industria y Competitividad of the Spanish Government and the European Regional Development Fund (AIE/FEDER) under projects TEC2013-48453 (MR-UHDTV), RTC-2015-3527-1 (BEGISE) and TEC2016-75981 (IVME).


  1. 1.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1975–1981 (2010)Google Scholar
  2. 2.
    Roshtkhari, M.J., Levine, M.D.: An on-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
  3. 3.
    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), pp. 1446–1453 (2009)Google Scholar
  4. 4.
    Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3449–3456 (2011)Google Scholar
  5. 5.
    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(11), 2287–2301 (2011)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Ryan, D., Denman, S., Fookes, C., Sridharan, S.: Textures of optical flow for real-time anomaly detection in crowds. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 230–235 (2011)Google Scholar
  8. 8.
    Nallaivarothayan, H., Fookes, C., Denman, S., Sridharan, S.: An MRF based abnormal event detection approach using motion and appearance features. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 343–348 (2014)Google Scholar
  9. 9.
    Tziakos, I., Cavallaro, A., Xu, L.Q.: Local abnormality detection in video using subspace learning. In: IEEE Conference on in Advanced Video and Signal Based Surveillance (AVSS), pp. 519–525 (2010)Google Scholar
  10. 10.
    Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439 (2010)Google Scholar
  11. 11.
    Nallaivarothayan, H., Ryan, D., Denman S., Sridharan, S., Fookes, C.: An evaluation of different features and learning models for anomalous event detection. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8 (2013)Google Scholar
  12. 12.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pp. 359–366 (2000)Google Scholar
  13. 13.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vision 92(1), 1–31 (2011)CrossRefGoogle Scholar
  14. 14.
    Yuan, Y., Fang, J., Wang, Q.: Online anomaly detection in crowd scenes via structure analysis. IEEE Trans. Cybern. 45(3), 548–561 (2015)CrossRefGoogle Scholar
  15. 15.
    Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  16. 16.
    Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhang, Y., Lu, H., Zhang, L., Ruan, X.: Combining motion and appearance cues for anomaly detection. Pattern Recogn. 51, 443–452 (2016)CrossRefGoogle Scholar
  18. 18.
    Saleemi, I., Hartung, L., Shah, M.: Scene understanding by statistical modeling of motion patterns. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2069–2076 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Grupo de Tratamiento de Imágenes, E.T.S.I. de TelecomunicaciónUniversidad Politécnica de MadridMadridSpain

Personalised recommendations