An Improved Method of Tracking and Counting Moving Objects Using Graph Cuts

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 355)

Abstract

To improve the efficiency of tracking and counting moving objects under occlusion conditions, an improved tracking and counting method is proposed. First, a graph cut method is employed to segment an image from a static scene, and foreground objects are identified by the sizes and positions of foreground areas obtained. Second, to distinguish moving objects, object classification based on shape is applied. In addition, in the object tracking phase, the proposed tracking method is used to calculate the centroid distance of neighboring objects and facilitate object tracking and people counting under occlusion conditions. In the experiments of moving object tracking and people counting in two video clips, compared with traditional methods, the experimental results show that the proposed method can increase the averaged detection ratio by approximately 11 %. Thus, the method can be used to reliably track and count.

Keywords

Graph cuts Object tracking Image segment Object classification 

Notes

Acknowledgments

This work was inancially supported by the Ministry of Education in China Project of the Humanities and Social Sciences (13YJCZH251).

References

  1. 1.
    Cristani M, Raghavendra R, et al. Human behavior analysis in video surveillance: a social signal processing perspective. Neurocomputing. 2013;100(1):86–97.CrossRefGoogle Scholar
  2. 2.
    Marcenaro L. Multiple object tracking under heavy occlusions by using Kalman filters based on shape matching. In: Proceedings of the IEEE ICIP; IEEE Press, Piscataway, NJ; 2002. p. 341–44.Google Scholar
  3. 3.
    Lien CC, Huang YL, et al. People counting using multi-mode multi-target tracking scheme. In: Proceedings of the IEEE IIH-MSP; IEEE Press, Piscataway, NJ; 2009. p. 1018–21.Google Scholar
  4. 4.
    Chan A, Liang Z, Vasconcelos N. Privacy preserving crowd monitoring: counting people without people models or tracking. CVPR; IEEE Press, Piscataway, NJ; 2008. p. 1–7.Google Scholar
  5. 5.
    Fehr D. Counting people in groups. In: Proceedings of the AVSS; IEEE Press, Piscataway, NJ; 2009. p. 152–57.Google Scholar
  6. 6.
    Zhang MJ, Kang BS. Modified object tracking and counting method based on gaussian mixture model. ICMRA; TTP, Switzerland; 2013. p. 598–602.Google Scholar
  7. 7.
    Boykov Y, Jolly MP. Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proceedings of the IEEE International Conference on Computer Vision; IEEE Press, Piscataway, NJ; 2001. p. 105–12.Google Scholar
  8. 8.
    Hu MK. Visual pattern recognition by moment invariants. IEEE Trans Inf Theory. 1962;8:179–87.MATHGoogle Scholar
  9. 9.
    Cristianini N, Shawe-Taylor J. Introduction to support vector machines. Cambridge: Cambridge University Press; 2000. p. 22–6.Google Scholar
  10. 10.
    Yilmaz A, Javed O, et al. Object tracking: a survey. ACM Comput Surv. 2006;38(4):22–32.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina
  2. 2.School of Economics and ManagementXi’an University of Post and TelecommunicationsXi’anChina

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