An Automatic People Counter in Stores Using a Low-Cost IoT Sensing Platform

  • Supatta ViriyavisuthisakulEmail author
  • Parinya Sanguansat
  • Satoshi Toriumi
  • Mikihara Hayashi
  • Toshihiko Yamasaki
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 81)


In this paper, we propose an automatic people counting system by using our low-cost Internet-of-Things (IoT) platform consisting of a single camera and Raspberry Pi. In this system, we count the number of moving people in bidirection by observing from a side view. Because the system can determine the height of the people, our system can be used to classify them into adults or children. This system is applied for no people overlapping problem in indoor environment only. The background subtraction and morphological operations are used to extract foreground objects from background images. The experimental results show proposed method can achieve 98% of people counting accuracy. It can also achieve 91% accuracy in adult/child classification. Although the algorithms for the people counting and classification are not novel, our technical contribution is that we have implemented them onto our IoT platform, whose cost is less than 100 US dollars. In addition, the images do not need be sent to the server, but all the image processing is done inside the device and only the results are uploaded to the server. This system can be applied to for customer behavior analysis or security.


People counting IoT sensing Raspberry Pi 



This is a collaboration project for joint internship program among Panyapiwat Institute of Management, Thailand, Future Standard Company and Yamasaki Lab of The University of Tokyo, Japan.


  1. 1.
    Xu, X.W., Wang, Z.Y., Liang, Y.H., Zhang, Y.Q.: A rapid method for passing people counting in monocular video sequences. In: 2007 International Conference on Machine Learning and Cybernetics, pp. 1657–1662. IEEE (2007)Google Scholar
  2. 2.
    Barandiaran, J., Murguia, B., Boto, F.: Real-time people counting using multiple lines. In: 2008 Proceedings of the Ninth International Workshop on Image Analysis for Multimedia Interactive Services, pp. 159–162. IEEE (2008)Google Scholar
  3. 3.
    Lee, K.Z., Tsai, L.W., Hung, P.C.: Fast people counting using sampled motion statistics. In: 2012 Proceedings of the Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 162–165. IEEE, July 2012Google Scholar
  4. 4.
    Cao, J., Sun, L., Odoom, M.G., Luan, F., Song, X.: Counting people by using a single camera without calibration. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 2048–2051. IEEE, May 2016Google Scholar
  5. 5.
    Bamrungthai, P., Puengsawad, S.: Robust people counting using a region-based approach for a monocular vision system. In: 2015 International Conference on Science and Technology (TICST), pp. 309–312. IEEE, November 2015Google Scholar
  6. 6.
    KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Video-Based Surveillance Systems, pp. 135–144. Springer, Boston (2002)Google Scholar
  7. 7.
    Sahoo, A.K., Patnaik, S., Biswal, P.K., Sahani, A.K., Mohanta, P.B.: An efficient algorithm for human tracking in visual surveillance system. In: 2013 IEEE Second International Conference on Image Information Processing, ICIIP 2013, pp. 125–130. IEEE, December 2013Google Scholar
  8. 8.
    Perng, J.W., Wang, T.Y., Hsu, Y.W., Wu, B.F.: The design and implementation of a vision-based people counting system in buses. In: 2016 International Conference on System Science and Engineering (ICSSE), pp. 1–3. IEEE, July 2016Google Scholar
  9. 9.
    Yufeng, X., Qiuyu, Z., Baozhu, Z.: People counting system based on improved Gaussian background model. In: 2015 International Conference on Smart and Sustainable City and Big Data (ICSSC), p. 5. Institution of Engineering and Technology (2015)Google Scholar
  10. 10.
    Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Supatta Viriyavisuthisakul
    • 1
    Email author
  • Parinya Sanguansat
    • 1
  • Satoshi Toriumi
    • 2
  • Mikihara Hayashi
    • 2
  • Toshihiko Yamasaki
    • 3
  1. 1.Panyapiwat Institute of ManagementNonthaburiThailand
  2. 2.Future Standard Co., Ltd.TokyoJapan
  3. 3.The University of TokyoTokyoJapan

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