Skip to main content
SpringerLink
Log in

Counting moving persons in crowded scenes

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

The paper presents a method for estimating the number of moving people in a scene for video surveillance applications. The method performance has been characterized on the public database used for the PETS 2009 and 2010 international competitions; the proposed method has been compared, on the same database, with the PETS competitions participants. The system exhibits a high accuracy, and revealed to be so fast that it can be used in real time surveillance applications. The rationale of the method lies on the extraction of suited scale-invariant feature points and the successive selection among them of the moving ones, under the hypothesis that the latter are associated to moving people. The perspective distortions are taken into account by dividing the input frames into smaller horizontal zones, each having (approximately) the same perspective effects. Therefore, the evaluation of the number of people is separately carried out for each zone, and the results are summed up. The most important peculiarity of the proposed method is the availability of a simple training procedure using a brief video sequence that shows a person walking around in the scene; the procedure automatically evaluates all the parameters needed by the system, thus making the method particularly suited for end-user applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Albiol, A., Silla, M.J., Albiol, A., Mossi, J.M.: Video analysis using corner motion statistics. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 31–38 (2009)

  2. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Google Scholar 

  3. Brostow, G.J., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: IEEE Conf. on Computer Vision and, Pattern Recognition, pp. 594–601 (2006)

  4. Chan, A.B., Liang, Z.S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–7 (2008)

  5. Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. Pattern Anal. Machine Intell. 30:909–926 (2008). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70738

    Google Scholar 

  6. Cho, S.Y., Chow, T.W.S., Leung, C.T.: A neural-based crowd estimation by hybrid global learning algorithm. IEEE Trans. Syst. Man Cybern. B 29(4), 535–541 (1999)

    Article  Google Scholar 

  7. Conte, D., Foggia, P., Percannella, G., Tufano, F., Vento, M.: A method for counting people in crowded scenes. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 225–232 (2010). doi:10.1109/AVSS.2010.78

  8. Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1988)

  9. Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: International Conference on, Pattern Recognition, pp. 1187–1190 (2006)

  10. Love, N.S., Kamath, C.: An empirical study of block matching techniques for the detection of moving objects. Tech. Rep. UCRL - TR - 218038, University of California, Lawrence Livermore National Laboratory (2006)

  11. Marana, A.N., da F. Costa, L., Lotufo, R.A., Velastin, S.A.: Estimating crowd density with mikowski fractal dimension. In: Int. Conf. on Acoustics, Speech and, Signal Processing (1999)

  12. Mikolajczyk, K., Schmid, C.: Scale and Affine Invariant Interest Point Detectors. Int. J. Comput. Vision 60(1), 63–86 (2004). doi:10.1023/B:VISI.0000027790.02288.f2

  13. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  14. PETS: http://www.cvg.rdg.ac.uk/PETS2009/ (2009)

  15. 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)

  16. Rittscher, J., Tu, P., Krahnstoever, N.: Simultaneous estimation of segmentation and shape. In: IEEE Conf. on Computer Vision and, Pattern Recognition, pp. 486–493 (2005)

  17. Tan, S., Dale, J., Anderson, A., Johnston, A.: Inverse perspective mapping and optic flow: a calibration method and a quantitative analysis. Image Vision Comput. 24, 153–165 (2006)

    Article  Google Scholar 

  18. Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1198–1211 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione,Ingegneria Elettrica e Matematica Applicata, Università di Salerno, Via Ponte don Melillo, 84084, Fisciano (SA), Italy

    Donatello Conte, Pasquale Foggia, Gennaro Percannella & Mario Vento

Authors
  1. Donatello Conte
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pasquale Foggia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gennaro Percannella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mario Vento
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Donatello Conte.

Additional information

This research has been partially supported by A.I.Tech s.r.l., a spin-off company of the University of Salerno (http://www.aitech-solutions.eu).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Conte, D., Foggia, P., Percannella, G. et al. Counting moving persons in crowded scenes. Machine Vision and Applications 24, 1029–1042 (2013). https://doi.org/10.1007/s00138-013-0491-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0491-3

Keywords

Search

Navigation