Skip to main content
Springer Nature Link
Log in

Automatic Counting of People Entering and Leaving Based on Dominant Colors and People Silhouettes

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

Included in the following conference series:

  • 848 Accesses

Abstract

Counting of people crowd is an important process for video surveillance, anomaly warning, public security control, as well as ongoing protection of people and public facilities. People counting methods are also applied for controlling the numbers of people entering and leaving such places as a tourist bus, an office or an university building, a public building, a supermarket or shopping mall, a culture or sport center, etc. The problem arises when the number of exiting people is not equal to the number of entering people. How many people are missing and who is missing? The paper presents an approach for people counting and detection of missing persons. This approach includes two procedures. First, the dominant color of people detected on video was analyzed. Next, the silhouette sizes were used. Both procedures finally allow us to define specific features distinctive for missing people. The results of tests performed with video recordings of people entering and exiting through the door are promising. In this approach individual’s identities are not registered; therefore, privacy violation is avoided.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 613–627. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_47

    Chapter  Google Scholar 

  2. Jingying, W.: A survey on crowd counting methods and datasets. In: Bhatia, S.K., Tiwari, S., Ruidan, S., Trivedi, M.C., Mishra, K.K. (eds.) Advances in Computer, Communication and Computational Sciences. AISC, vol. 1158, pp. 851–863. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-4409-5_76

    Chapter  Google Scholar 

  3. Li, B., Huang, H., Zhang, A., Liu, P., Liu, C.: Approaches on crowd counting and density estimation: a review. Pattern Anal. Appl. 24(3), 853–874 (2021)

    Article  Google Scholar 

  4. Sindagi, V.A., Patel, V.M.: A survey of recent advances in CNN-based single image crowd counting and density estimation. Pattern Recogn. Lett. 107, 3–16 (2018)

    Article  Google Scholar 

  5. Fan, Z., Zhang, H., Zhang, Z., Lu, G., Zhang, Y., Wang, Y.: A survey of crowd counting and density estimation based on convolutional neural network. Neurocomputing 472, 224–251 (2022)

    Article  Google Scholar 

  6. Lin, S.F., Chen, J.Y., Chao, H.X.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Humans 31(6), 645–654 (2001)

    Article  Google Scholar 

  7. Loy, C.C., Chen, K., Gong, S., Xiang, T.: Crowd counting and profiling: methodology and evaluation. In: Ali, S., Nishino, K., Manocha, D., Shah, M. (eds.) Modeling, Simulation and Visual Analysis of Crowds. TISVC, vol. 11, pp. 347–382. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-8483-7_14

    Chapter  Google Scholar 

  8. Zhang, Z., Wang, M., Geng, X.: Crowd counting in public video surveillance by label distribution learning. Neurocomputing 166, 151–163 (2015)

    Article  Google Scholar 

  9. Choroś, K.: Further tests with click, block, and heat maps applied to website evaluations. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011. LNCS (LNAI), vol. 6923, pp. 415–424. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23938-0_42

    Chapter  Google Scholar 

  10. Fan, Z., Zhu, Y., Song, Y., Liu, Z.: Generating high quality crowd density map based on perceptual loss. Appl. Intell. 50(4), 1073–1085 (2019). https://doi.org/10.1007/s10489-019-01573-7

    Article  Google Scholar 

  11. Kang, D., Ma, Z., Chan, A.B.: Beyond counting: comparisons of density maps for crowd analysis tasks – counting, detection, and tracking. IEEE Trans. Circuits Syst. Video Technol. 29(5), 1408–1422 (2018)

    Article  Google Scholar 

  12. Pham, V.Q., Kozakaya, T., Yamaguchi, O., Okada, R.: COUNT forest: CO-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision ICCV, pp. 3253–3261 (2015)

    Google Scholar 

  13. Ryan, D., Denman, S., Fookes, C., Sridharan, S.: Crowd counting using multiple local features. In: Proceedings of the Digital Image Computing: Techniques and Applications DICTA, pp. 81–88. IEEE (2009)

    Google Scholar 

  14. Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: Deep people counting in extremely dense crowds. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1299–1302 (2015)

    Google Scholar 

  15. Ranjan, V., Le, H., Hoai, M.: Iterative crowd counting. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 278–293. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_17

    Chapter  Google Scholar 

  16. Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019)

    Google Scholar 

  17. Snidaro, L., Micheloni, C., Chiavedale, C.: Video security for ambient intelligence. IEEE Trans. Syst., Man, Cybern. - Part A: Syst. Humans 35(1), 133–144 (2005). https://doi.org/10.1109/TSMCA.2004.838478

    Article  Google Scholar 

  18. Rabaud, V., Belongie, S.: Counting crowded moving objects. In: Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition CVPR’06, vol. 1, pp. 705–711. IEEE (2006)

    Google Scholar 

  19. Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  20. Park, J.H., Cho, S.I.: Flow analysis-based fast-moving flow calibration for a people-counting system. Multimedia Tools Appl. 80(21–23), 31671–31685 (2021). https://doi.org/10.1007/s11042-021-11231-1

    Article  Google Scholar 

  21. Marciniak, T., Dąbrowski, A., Chmielewska, A., Nowakowski, M.: Real-time bi-directional people counting using video blob analysis. In: Proceedings of the Joint Conference New Trends in Audio & Video and Signal Processing: Algorithms, Architectures, Arrangements and Applications NTAV/SPA, pp. 161–166. IEEE (2012)

    Google Scholar 

  22. Budge, S.E., Sallay, J.A., Wang, Z., Gunther, J.H.: People matching for transportation planning using texel camera data for sequential estimation. IEEE Trans. Syst., Man, Cybernet.: Syst. 43(3), 619–629 (2012)

    Article  Google Scholar 

  23. Nasir, A.S.A., Gharib, N.K.A., Jaafar, H.: Automatic passenger counting system using image processing based on skin colour detection approach. In: Proceedings of the International Conference on Computational Approach in Smart Systems Design and Applications ICASSDA, pp. 1–8. IEEE (2018)

    Google Scholar 

  24. Cho, S.I., Kang, S.J.: Real-time people counting system for customer movement analysis. IEEE Access 6, 55264–55272 (2018)

    Article  Google Scholar 

  25. Hsu, Y.W., Wang, T.Y., Perng, J.W.: Passenger flow counting in buses based on deep learning using surveillance video. Optik – Int. J. Light Electron Optics 202, 163675 (2020)

    Article  Google Scholar 

  26. Zhao, J., Li, C., Xu, Z., Jiao, L., Zhao, Z., Wang, Z.: Detection of passenger flow on and off buses based on video images and YOLO algorithm. Multimed. Tools Appl. 81(4), 4669–4692 (2022). https://doi.org/10.1007/s11042-021-10747-w

    Article  Google Scholar 

  27. Rosebrock, A.: OpenCV people counter with Python, Object Tracking Tutorials. https://pyimagesearch.com/2018/08/13/opencv-people-counter/ (2021)

Download references

Author information

Authors and Affiliations

  1. Faculty of Information and Communication Technology, Department of Applied Informatics, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370, Wrocław, Poland

    Kazimierz Choroś & Maciej Uran

Authors
  1. Kazimierz Choroś
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maciej Uran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kazimierz Choroś .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Choroś, K., Uran, M. (2022). Automatic Counting of People Entering and Leaving Based on Dominant Colors and People Silhouettes. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21967-2_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics