Abstract
Crowd counting is of great importance to many applications in various scenarios. Wi-Fi Channel State Information (CSI)-based crowd counting is a highly accurate privacy-conscious method. However, the problem with CSI-based crowd counting is the size and cost of the CSI collecting tool. Most studies benefiting from CSI collection use laptops with specific Network Interface Cards (NICs). The size and cost of the laptops restrict the practicability of such systems and limit active repositioning and mobility of the devices. This research aims to realize highly accurate CSI-based crowd counting using only one pair of lightweight and low-cost IoT devices. The devices are very agile and can easily be deployed even in space-limited environments. However, they have the disadvantage of poor data transportation compared to laptops. We compensate for this drawback by adjusting the deployment location, using multiple preprocessing methods depending on the situation, and standardizing the data for each subcarrier. We conducted evaluations of crowd counting in two representative scenarios. For the scenario of crowd sizes of 0, 1, 2, and 3 persons, when we used a weighted moving average (WMA) filter and phase sanitization as the preprocessing methods, the accuracy was 70.3%. When we used percentage of nonzero elements (PEM) and a moving average (MA) filter as the preprocessing methods, the accuracy was 84.6%. For the scenario of crowd sizes of 0, 5, 10, 15, and 20 persons, when we used a WMA filter and phase sanitization as the preprocessing methods, the accuracy was 76.5%. When we used PEM and a MA filter as the preprocessing methods, the accuracy was 75.9%. We found that the appropriate preprocessing method differs between the case of a small number of people and the case of a large number of people.
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References
Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by mid-based foreground segmentation and head-shoulder detection. In: 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA, pp. 1–4. IEEE (2008). https://doi.org/10.1109/ICPR.2008.4761705
Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003), Fort Worth, TX, USA, pp. 407–415. IEEE (2003). https://doi.org/10.1109/PERCOM.2003.1192765
Weppner, J., Lukowicz, P.: Bluetooth-based collaborative crowd density estimation with mobile phones. In: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), San Diego, CA, USA, pp. 193–200. IEEE (2013). https://doi.org/10.1109/PerCom.2013.6526732
Choi, J., Ge, H., Koshizuka, N.: IoT-based occupants counting with smart building state variables. In: 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Bayonne, France, pp. 171–176. IEEE (2020). https://doi.org/10.1109/WETICE49692.2020.00041
Doong, S.H.: Spectral human flow counting with RSSI in wireless sensor networks. In: 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS), Washington, DC, USA, pp. 110–112. IEEE (2016). https://doi.org/10.1109/DCOSS.2016.33
Tse, D., Viswanath, P.: Fundamentals of Wireless Communication. Cambridge University Press, New York (2005)
Liu, S., Zhao, Y., Chen, B.: WiCount: a deep learning approach for crowd counting using WiFi signals. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), Guangzhou, China, pp. 967–974. IEEE (2017). https://doi.org/10.1109/ICCCN.2018.8487420
Zhao, Y., Liu, S., Xue, F., Chen, B., Chen, X.: DeepCount: crowd counting with Wi-Fi using deep learning. J. Commun. Inf. Networks 4(3), 38–52 (2019). https://doi.org/10.23919/JCIN.2019.8917884
Xi, W., et al.: Electronic frog eye: counting crowd using WiFi. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, Toronto, ON, Canada, pp. 361–369. IEEE (2014). https://doi.org/10.1109/INFOCOM.2014.6847958
Sandaruwan, R., Alagiyawanna, I., Sandeepa, S., Dias, S., Dias, D.: Device-free pedestrian count estimation using Wi-Fi channel state information. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, pp. 2610–2616. IEEE (2021). https://doi.org/10.1109/ITSC48978.2021.9564725
Hernandez, S.M., Bulut, E.: Lightweight and standalone IoT-based WiFi sensing for active repositioning and mobility. In: 2020 IEEE 21st International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Cork, Ireland, pp. 277–286. IEEE (2020). https://doi.org/10.1109/WoWMoM49955.2020.00056
Atif, M., Muralidharan, S., Ko, H., Yoo, B.: Wi-ESP-a tool for CSI-based Device-Free Wi-Fi Sensing (DFWS). J. Comput. Des. Eng. 7(5), 644–656 (2020). https://doi.org/10.1093/jcde/qwaa048
Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Tool release: gathering 802.11n traces with channel state information. ACM SIGCOMM CCR 41(1), 53 (2011)
Xie, Y., Li, Z., Li, M.: Precise power delay profiling with commodity WiFi. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 53–64. MobiCom 2015, ACM, New York, USA (2015). https://doi.org/10.1145/2789168.2790124
Espressif Systems: ESP-IDF programming guide (2021). https://docs.espressif.com/projects/esp-idf/en/latest/esp32/. Accessed 2 Feb 2022
Jiang, W., Liu, Y., Lei, Y., Wang, K., Yang, H., Xing, Z.: For better CSI fingerprinting based localization: a novel phase sanitization method and a distance metric. In: 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, Australia, pp. 1–7. IEEE (2017). https://doi.org/10.1109/VTCSpring.2017.8108351
Sen, S., Radunovic, B., Choudhury, R.R., Minka, T.: You are facing the Mona Lisa: spot localization using PHY layer information. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp. 183–196. MobiSys 2012, Association for Computing Machinery, New York, USA (2012). https://doi.org/10.1145/2307636.2307654
Acknowledgments
We would like to thank all the participants in the experiments. We also thank Masahiro Matsui and Yusuke Sasaki for their assistance with developing the prototype in the early stages of research. Our research was conducted at Mitsui Fudosan UTokyo Laboratory, a joint research project between Mitsui Fudosan Co., Ltd. and The University of Tokyo. We gratefully acknowledge the kind support of Mitsui Fudosan Co., Ltd.
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Kitagishi, T., Hangli, G., Michikata, T., Koshizuka, N. (2022). Wi-Monitor: Wi-Fi Channel State Information-Based Crowd Counting with Lightweight and Low-Cost IoT Devices. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham. https://doi.org/10.1007/978-3-031-20936-9_11
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