Crowd counting is of great importance to many applications. Conventional vision-based approaches require line of sight and pose privacy concerns, while most radio-based approaches involve high deployment cost. In this paper, we propose to utilize WiFi channel state information (CSI) to infer crowd count in a device-free way, with only one pair of WiFi transmitter and receiver. The proposed method establishes the statistical relationship between the variation of CSI and the number of people with deep neural networks (DNN) and thereafter estimates the people count according to the real-time CSI through the trained DNN model. Evaluations demonstrate the effectiveness of the method. For the crowd size of 6, the counting error was within 1 person for 100% of the cases. For the crowd size of 34, the counting error was within 1 person for 97.7% of the cases and within 2 persons for 99.3% of the cases.
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Li, M., Zhang, Z., Huang, K., & Tan, T. (2008). 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 (pp. 1–4).
Kim, M., Kim, W., & Kim, C. (2011). Estimating the number of people in crowded scenes. Proceedings of SPIE, 7882(23), 78 820L–78 820L-8.
Kannan, P. G., Venkatagiri, S. P., Chan, M. C., Ananda, A.L., & Peh, L.-S. (2012). Low cost crowd counting using audio tones. In Proceedings of the 10th ACM conference on embedded network sensor systems (SenSys’12) (pp. 155–168). ACM.
Weppner, J., & Lukowicz, P. (2013). Bluetooth based collaborative crowd density estimation with mobile phones. In 2013 IEEE international conference on pervasive computing and communications (PerCom) (pp. 193–200).
Yuan, Y., Zhao, J., Qiu, C., & Xi, W. (2013). Estimating crowd density in an RF-based dynamic environment. IEEE Sensors Journal, 13(10), 3837–3845.
Doong, S. H. (2016). Spectral human flow counting with RSSI in wireless sensor networks. In 2016 international conference on distributed computing in sensor systems (DCOSS) (pp. 110–112).
Xu, C., Firner, B., Moore, R. S., Zhang, Y., Trappe, W., Howard, R., Zhang, F., & An, N. (2013). SCPL: Indoor device-free multi-subject counting and localization using radio signal strength. In 2013 ACM/IEEE IPSN (pp. 79–90).
Lv, H., Liu, M., Jiao, T., Zhang, Y., Yu, X., Li, S., Jing, X., & Wang, J. (2013). Multi-target human sensing via UWB bio-radar based on multiple antennas. In TENCON 2013 (pp. 1–4).
He, J., & Arora, A. (2014). A regression-based radar-mote system for people counting. In 2014 PerCom (pp. 95–102).
Cianca, E., Sanctis, M. D., & Domenico, S. D. (2017). Radios as sensors. IEEE Internet of Things Journal, 4(2), 363–373.
Nakatsuka, M., Iwatani, H., & Katto, J. (2008). A study on passive crowd density estimation using wireless sensors. In 2008 international conference on mobile computing and ubiquitous networking
Depatla, S., Muralidharan, A., & Mostofi, Y. (2015). Occupancy estimation using only wifi power measurements. IEEE Journal on Selected Areas in Communications, 33(7), 1381–1393.
Abdel-Nasser, H., Samir, R., Sabek, I., & Youssef, M. (2013). MonoPHY: Mono-stream-based device-free WLAN localization via physical layer information. In 2013 IEEE WCNC (pp. 4546–4551).
Wu, K., Xiao, J., Yi, Y., Chen, D., Luo, X., & Ni, L. M. (2013). CSI-based indoor localization. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1300–1309.
Xi, W., Zhao, J., Li, X. -Y, Zhao, K., Tang, S., Liu, X., & Jiang, Z. (2014). Electronic frog eye: Counting crowd using WiFi. In 2014 IEEE INFOCOM (pp. 361–369).
Di Domenico, S., De Sanctis, M., Cianca, E., & Bianchi, G. (2016). A trained-once crowd counting method using differential wifi channel state information. In 2016 WPA (pp. 37–42). ACM.
Domenico, S. D.. Pecoraro, G., Cianca, E., & Sanctis, M. D. (2016). Trained-once device-free crowd counting and occupancy estimation using WiFi: A doppler spectrum based approach. In 2016 WiMob (pp. 1–8).
Halperin, D., Hu, W., Sheth, A., & Wetherall, D. (2010). Predictable 802.11 packet delivery from wireless channel measurements. In: 2010 ACM SIGCOMM (pp. 159–170). ACM.
Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv:1804.02767.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2015). SSD: Single shot multibox detector. arXiv:1512.02325.
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Zhou, R., Lu, X., Fu, Y. et al. Device-free crowd counting with WiFi channel state information and deep neural networks. Wireless Netw (2020). https://doi.org/10.1007/s11276-020-02274-7
- Crowd counting
- Channel state information
- Deep neural networks