Skip to main content
SpringerLink
Log in

People Counting in Conference Scenes in Shearlet Domain

  • Conference paper
  • First Online:
Nature of Computation and Communication (ICTCC 2016)

Included in the following conference series:

  • 626 Accesses

Abstract

People counting is an important task in visual-based surveillance system. The task of people counting is not easy to solve problems. In this paper, the author has proposed a method for people counting which identify the objects present in a scene of conference into two classes: empty seat and non-empty seat. The proposed method based on saliency map and color smoothing in shearlet domain. The author uses shearlet transform and combine of adaboost with support vector machine for classifiers and people counting. The proposed method is simple but the accuracy of people counting is high.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cai, Z., Yu, Z.L., Liu, H., Zhang, K.: Counting people in crowded scenes by video analyzing. In: IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1841–1845 (2014)

    Google Scholar 

  2. Hou, Y.-L., Pang, G.K.: People counting and human detection in a challenging situation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(1), 24–33 (2011)

    Article  Google Scholar 

  3. Chan, A.B., Vasconcelos, N.: Counting people with low-level features and Bayesian regression. IEEE Trans. Image Process. 21, 2160–2177 (2012)

    Article  MathSciNet  Google Scholar 

  4. Xu, H., Lv, P., Meng, L.: A people counting system based on head-shoulder detection and tracking in surveillance video. In: IEEE International Conference on Computer Design and Applications (ICCDA), vol. 1, pp. V1–V394 (2010)

    Google Scholar 

  5. Chen, L., Wu, H., Zhao, S., Gu, J.: Head-shoulder detection using joint hog features for people counting and video surveillance in library. In: IEEE Workshop on Electronics, Computer and Applications, pp. 429–432 (2014)

    Google Scholar 

  6. Bala Subburaman, V., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2012)

    Google Scholar 

  7. Teixeira, T., Savvides, A.: Lightweight people counting and localizing in indoor spaces using camera sensor nodes. In: First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2007, pp. 36–43 (2007)

    Google Scholar 

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

    Google Scholar 

  9. Wang, J., Fu, W., Liu, J., Lu, H.: Spatio-temporal group context for pedestrian counting. IEEE Trans. Circuits Syst. Video Technol., 1–11 (2014)

    Google Scholar 

  10. Zhang, E., Chen, F.: A fast and robust people counting method in video surveillance. In: International Conference on Computational Intelligence and Security, pp. 339–343. IEEE (2007)

    Google Scholar 

  11. Luo, J., Wang, J., Xu, H., Lu, H.: A real-time people counting approach in indoor environment. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8935, pp. 214–223. Springer, Heidelberg (2015). doi:10.1007/978-3-319-14445-0_19

    Google Scholar 

  12. Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int. J. Comput. Vis. 75(2), 247–266 (2007)

    Article  Google Scholar 

  13. Kong, D., Gray, D., Hat, T.: A viewpoint invariant approach for crowd counting. In: Proceedings of the International Conference on Pattern Recognition, pp. 1187–1190 (2006)

    Google Scholar 

  14. Kutyniok, G., Labate, D.: Shearlets: Multiscale Analysis for Multivariate Data. Applied and Numerical Harmonic Analysis. Birkhauser (2012)

    Google Scholar 

  15. Lim, W.-Q.: The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans. Image Process. 19(5), 1166–1180 (2010)

    Article  MathSciNet  Google Scholar 

  16. Guo, K., Labate, D.: Optimally sparse multidimensional representation using shearlets. SIAM J. Math. Anal. 39, 298–318 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  17. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  18. Patel, V.M., Easley, G.R., Healy Jr., D.M.: Shearlet-based deconvolution. IEEE Trans. Image Process. 18(12), 2673–2685 (2009)

    Article  MathSciNet  Google Scholar 

  19. Thanh Binh, N., Khare, A.: Object tracking of video sequences in curvelet domain. Int. J. Image Graph. 11(1), 1–20 (2011)

    Article  MathSciNet  Google Scholar 

  20. Zivkovic, Z., Krose, B.: An EM-like algorithm for color histogram-based object tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, USA, vol. 1, pp. 798–803 (2004)

    Google Scholar 

  21. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4) (2006)

    Google Scholar 

  22. Wang, D.: Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Trans. Circuits Syst. Video Technol. 8(5), 539–546 (1998)

    Article  Google Scholar 

  23. Li, X., Wang, L., Sung, E.: AdaBoost with SVM-based component classifiers. Eng. Appl. Artif. Intell. 21, 785–795 (2008)

    Article  Google Scholar 

  24. Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W., Thompson, P.M.: Comparison of AdaBoost and support vector machines for detecting Alzheimer’s disease through automated hippocampal segmentation. IEEE Trans. Med. Imaging 29(1), 30–43 (2010)

    Article  Google Scholar 

  25. Khare, M., Thanh Binh, N., Srivastava, R.K.: Dual tree complex wavelet transform based human object classification using support vector machine. J. Sci. Technol. 51(4B), 134–142 (2013)

    Google Scholar 

  26. Thanh Binh, N.: Object classification based on contourlet transform in outdoor environment. Nat. Comput. Commun. 144, 341–349 (2015)

    Article  Google Scholar 

Download references

Acknowledgment

This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2015-20-08.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Nguyen Thanh Binh

Authors
  1. Nguyen Thanh Binh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nguyen Thanh Binh .

Editor information

Editors and Affiliations

  1. Nguyen Tat Thanh University , Ho Chi Minh City, Vietnam

    Phan Cong Vinh

  2. Fukuoka Institute of Technology , Fukuoka, Japan

    Leonard Barolli

Rights and permissions

Reprints and permissions

Copyright information

© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Thanh Binh, N. (2016). People Counting in Conference Scenes in Shearlet Domain. In: Vinh, P., Barolli, L. (eds) Nature of Computation and Communication. ICTCC 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 168. Springer, Cham. https://doi.org/10.1007/978-3-319-46909-6_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46909-6_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46908-9

  • Online ISBN: 978-3-319-46909-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation