Texture-Based Crowd Detection and Localisation

  • Stefano Ghidoni
  • Grzegorz Cielniak
  • Emanuele Menegatti
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation.

Keywords

Crowd detection intelligent video surveillance 

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References

  1. 1.
    Marana, A.N., Velastin, S.A., Costa, L.F., Lotufo, R.A.: Estimation of crowd density using image processing. In: IEE Colloquium on Image Processing for Security Applications (Digest No.: 1997/074), pp. 11/1–11/8 (March 1997)Google Scholar
  2. 2.
    Davies, A.C., Hong Yin, J., Velastin, S.A.: Crowd monitoring using image processing. Electronics Communication Engineering Journal 7(1), 37–47 (1995)CrossRefGoogle Scholar
  3. 3.
    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 (June 2009)Google Scholar
  4. 4.
    Ihaddadene, N., Djeraba, C.: Real-time crowd motion analysis. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (December 2008)Google Scholar
  5. 5.
    Arandjelović, O.: Crowd detection from still images. In: Proc. British Machine Vision Conference (BMVC) (September 2008)Google Scholar
  6. 6.
    Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  7. 7.
    Rahmalan, H., Nixon, M.S., Carter, J.N.: On crowd density estimation for surveillance. In: The Institution of Engineering and Technology Conference on Crime and Security, pp. 540–545 (June 2006)Google Scholar
  8. 8.
    Kong, D., Gray, D., Tao, H.: Counting pedestrians in crowds using viewpoint invariant training. In: Proc. British Machine Vision Conference (BMVC) (September 2005)Google Scholar
  9. 9.
    Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1187–1190 (September 2006)Google Scholar
  10. 10.
    Gárate, C., Bilinsky, P., Bremond, F.: Crowd event recognition using hog tracker. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–6 (December 2009)Google Scholar
  11. 11.
    Marana, A.N., Costa, L.F., Lotufo, R.A., Velastin, S.A.: On the efficacy of texture analysis for crowd monitoring. In: Proceedings of SIBGRAPI 1998, International Symposium on Computer Graphics, Image Processing, and Vision, pp. 354–361 (October 1998)Google Scholar
  12. 12.
    McKenna, S.J., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Computer Vision and Image Understanding 80(1), 42–56 (2000)MATHCrossRefGoogle Scholar
  13. 13.
    Gennari, G., Hager, G.D.: Probabilistic data association methods in visual tracking of groups. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II–876–II–881 (June 2004)Google Scholar
  14. 14.
    Lau, B., Arras, K.O., Burgard, W.: Tracking groups of people with a multi-model hypothesis tracker. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 3180–3185 (May 2009)Google Scholar
  15. 15.
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  16. 16.
    Friedman, J.H., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Dept. of Statistics, Stanford University (1998)Google Scholar
  17. 17.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees, 1st edn. Wadsworth and Brooks, Monterey (1984)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefano Ghidoni
    • 1
  • Grzegorz Cielniak
    • 2
  • Emanuele Menegatti
    • 1
  1. 1.Intelligent Autonomous Systems Laboratory (IAS-Lab) Department of Information EngineeringThe University of PadovaPadovaItaly
  2. 2.School of Computer ScienceUniversity of LincolnBrayford PoolUK

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