Advertisement

The Large-Scale Crowd Density Estimation Based on Effective Region Feature Extraction Method

  • Hang Su
  • Hua Yang
  • Shibao Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

This paper proposes an intelligent video surveillance system to estimate the crowd density by effective region feature extracting (ERFE) and learning. Firstly, motion detection method is utilized to segment the foreground, and the extremal regions of the foreground are then extracted. Furthermore, a new perspective projection method is proposed to modify the 3D to 2D distortion of the extracted regions, and the moving cast shadow is eliminated based on the color invariant of the shadow region. Afterwards, histogram statistic method is applied to extract crowd features from the modified regions. Finally, the crowd features are classified into a range of density levels by using support vector machine. Experiments on real crowd videos show that the proposed density estimation system has great advantage in large-scale crowd analysis. And more importantly, better performance is achieved even on variant view angle or illumination changing conditions. Thus the video surveillance system is more robust and practical.

Keywords

Support Vector Machine Density Level Video Surveillance System Shadow Detection Crowd Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schofer, J., Ushpiz, A., Polus, A.: Pedestrian Flow and Level of Service. J. Transportation Eng. 109, 46–56 (1983)CrossRefGoogle Scholar
  2. 2.
    Davies, A.C., Yin, J.H., Velastin, S.A.: Crowd monitoring using image processing. Electronics and Communication Engineering Journal 7, 37–47 (1995)CrossRefGoogle Scholar
  3. 3.
    Chow T.W.S., Cho, S.-Y.: Industrial neural vision system for underground railway station platform surveillance. Advanced Engineering Informatics (2002)Google Scholar
  4. 4.
    Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd Density Estimation Using Texture Analysis and Learning. In: IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006)Google Scholar
  5. 5.
    Sen, G., Wei, L., Ping, Y.H.: Counting people in crowd open scene based on grey level dependence matrix. In: International Conference on Information and Automation, pp. 228–231 (2009)Google Scholar
  6. 6.
    Verona, V.V., Marana, A.N.: Wavelet packet analysis for crowd density estimation. In: Proceedings of the IASTED International Symposia on Applied Informatics, Innsbruck, Austria, pp. 535–540 (2001)Google 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 (2006), pp. 540–545Google Scholar
  8. 8.
    Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 10(5), 1055–1064 (1999)CrossRefGoogle Scholar
  9. 9.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, vol. 1, pp. 384–393 (2002)Google Scholar
  10. 10.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1), 43–72 (2005)CrossRefGoogle Scholar
  11. 11.
    Pi, M.H., Zhang, H.: Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed. In: Proceedings of the 2nd Canadian Conference on Computer and Robot Vision (CRV 2005), pp. 57–64. IEEE Computer Society, Washington (2005)Google Scholar
  12. 12.
    Jacques, C.S., Jung, C.R., Musse, S.R.: A background subtraction model adapted to illumination changes. In: IEEE Conference on Image Processing, pp: 1817–1820 (October 2006)Google Scholar
  13. 13.
    Kres, R., Ulrich, H.-G.: Pairwise classification and support vector machines. In: Advances in Kernel Methods: Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hang Su
    • 1
    • 2
  • Hua Yang
    • 1
    • 2
  • Shibao Zheng
    • 1
    • 2
  1. 1.Institution of Image Communication and Information Processing, Department of EEShanghai Jiaotong UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Digital Media Processing and TransmissionChina

Personalised recommendations