Abstract
In the current era, the Internet of Things is developing rapidly. The increasing number of persons pay attention to personnel information in an area. Crowd counting is favored by many researchers. It can be applied in many people-centric scenarios, such as the smart home and supermarket energy management. In this paper, we only use a pair of transceivers, relying on the Received Signal Strength Indicator (RSSI) information of the commercial WiFi signal to count the crowd without requiring the people carry any device. We first model the received signal into three parts, which are the Line-of-Sight (LoS) path blockage effect, Multipath (MP) effect on the received signal, and the multipath effect resulting from signal reflection by the fixed objects. Then, we analyze the Probability Density Function (PDF) of the received signal based on the characteristic function and then combine two different distributions to characterize the relationship between the number of persons and the PDF of the received signal amplitude. Finally, we use the Dynamic Time Warping (DTW) algorithm for crowd counting. We validate the performance of the approach in an outdoor environment, and the experimental results show that our approach can count four persons with an average accuracy of 96.25%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cardone, G., Cirri, A., Corradi, A., Foschini, L., Ianniello, R., Montanari, R.: Crowdsensing in urban areas for city-scale mass gathering management: geofencing and activity recognition. Journal 14(12), 4185–4195 (2014)
Shiwakoti, N., Xiaomeng, S., Zhirui, Y.: A review on the performance of an obstacle near an exit on pedestrian crowd evacuation. Journal 113, 54–67 (2019)
Wang, L., Yung, N.H.C.: Crowd counting and segmentation in visual surveillance. In: IEEE International Conference on Image Processing, pp. 2573–2576. IEEE, Cairo (2009)
Pham, V., Kozakaya, T., Yamaguchi, O., Okada, R.: COUNT forest: CO-Voting uncertain number of targets using random forest for crowd density estimation. In: 2015 IEEE International Conference on Computer Vision, pp. 3253–3261. IEEE, Santiago (2015)
Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554. IEEE, Portland (2013)
Wang, F., Zhang, F., Wu, C., Wang, B., Liu, K.J.R.: Respiration tracking for people counting and recognition. Journal 7(6), 5233–5245 (2020)
Kurkcu, A., Ozbay, K.: Estimating pedestrian densities, wait times, and flows with WiFi and bluetooth sensors. Journal 2644(1), 72–82 (2017)
Al-Zaydi, Z., Vuksanovic, B., Habeeb, I.: Image processing based ambient context-aware people detection and counting. Journal 8(3), 268–272 (2018)
Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4031–4039. IEEE, Honolulu (2017)
Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 833–841. IEEE, Boston (2015)
Prasertsung, P., Horanont, T.: How does coffee shop get crowded? Using WiFi footprints to deliver insights into the success of promotion. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 421–426. ACM, Maui (2017)
Weppner, J., Lukowicz, P.: Bluetooth based collaborative crowd density estimation with mobile phones. In: 2013 IEEE International Conference on Pervasive Computing and Communications, pp. 193–200. IEEE, San Diego (2013)
Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. Journal 10, 701–710 (2004)
Yuan, Y., Qiu, C., Xi, W., Zhao, J.: Crowd density estimation using wireless sensor networks. In: 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks, pp. 138–145. IEEE, Beijing (2011)
Bocca, M., Kaltiokallio, O., Patwari, N., Venkatasubramanian, S.: Multiple target tracking with RF sensor networks. Journal 13(8), 1787–1800 (2014)
Xi, W., et al.: Electronic frog eye: counting crowd using WiFi. In: IEEE Conference on Computer Communications, pp. 361–369. IEEE, Toronto (2014)
Depatla, S., Muralidharan, A., Mostofi, Y.: Occupancy estimation using only WiFi power measurements. Journal 33(7), 1381–1393 (2015)
Acknowledgement
This work is supported in part by the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800625, KJZD-K202000605), the Chongqing Natural Science Foundation Project (cstc2019jcyj-msxmX0635, cstc2020jcyj-msxmX0842), and the National Natural Science Foundation of China (61771083, 61771209).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, M., Yang, X., Jin, Y., Zhou, M. (2021). Dynamic Time Warping Based Passive Crowd Counting Using WiFi Received Signal Strength. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-78612-0_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78611-3
Online ISBN: 978-3-030-78612-0
eBook Packages: Computer ScienceComputer Science (R0)