Electronic frog eye: Counting crowd using WiFi

By Wei Xi, Jizhong Zhao, Xiangyang Li, Kun Zhao, Shaojie Tang, Xue Liu, Zhiping Jiang. IEEE Conference on Computer Communications, 2014

378 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9ed1cba2dd58c29cea4bf7a47d10743dccaea124

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Gang Zhou
2. Alex X. Liu
3. Wen Wang
4. Zheng Yang
5. Yunhao Liu
6. Chenshu Wu
7. Yongsen Ma
8. Shuangquan Wang

In Electrical and Electronic Engineering

1. Zheng Yang
2. Yunhao Liu
3. Gang Zhou
4. Chenshu Wu
5. Alex X. Liu
6. Zimu Zhou
7. Yongsen Ma
8. Shuangquan Wang

Citations in the corpus

• A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “(PEM) metric in the CSI matrix [Xi et al. 2014], yet it is still an open issue to count both static and moving targets using off-the-shelf wireless network infrastructures.”
• Building occupancy estimation and detection: A review (2018, methodology): “Another direct occupancy estimation system using WiFi was presented in [72].”
• Keystroke Recognition Using WiFi Signals (2015, methodology): “Some recent work uses CSI values to recognize variousmacro aspects of human movements such as falling down [6], household activities [7], detection of human presence [8], and estimating the number of people in a crowd [9].”
• E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, background): “, [36]) and to suspend operation during this time.”
• Non-Invasive Detection of Moving and Stationary Human With WiFi (2015, background): “Numerous research has explored to detect human movements non-invasively for localization and tracking [1], [3], counting [2], [26] or activity recognition [17].”
• RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices (2017, background): “H.Wang, D. Zhang, J. Ma, Y. Wang, and S. Li are with the Key Laboratory of High Confidence Software Technologies, Ministry of Education, and School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China.”
• Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi (2015, background): “, [23]) or entered manually from the users.”
• Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, methodology): “Xi et al. proposed to use CSI to count the number of people in a crowd [30].”
• Precise Power Delay Profiling with Commodity Wi-Fi (2015, methodology): “Such a resolution imposes inevitable uncertainty in mobility detection [2], [3], [4], [5], [17], gesture recognition [18], or localization [1], [6].”
• Wireless Sensing for Human Activity: A Survey (2020, methodology): “…[31]–[33], [46]–[48], [72]–[74], [74]–[83] CSI-based Channel conditions/properties of wireless links Fine-grained No [7]–[9], [9]–[11], [15], [16], [21]–[27], [34]–[41], [49]–[54], [67]–[71], [84]–[91] FMCW-based Frequency shift associated with human body depth Fine-grained Yes [28],…”
• WiFi Sensing with Channel State Information (2019, methodology): “Device-Free Human Localization/Tracking: Position [36, 52, 69, 74, 76, 93, 109, 148], Orientation [89, 130], Motion [41, 43, 115, 130], Walking Direction [63, 115, 126, 136], Step/Gait [97, 126], Hand Drawing [84, 130, 131], Speed [137] Device-based Human Localization/Tracking [46, 87, 123, 131] Obj”