Abstract
Since the outbreak of the world-wide novel coronavirus pandemic, crowd counting in public areas, such as in shopping centers and in commercial streets, has gained popularity among public health administrations for preventing the crowds from gathering. In this paper, we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information (CSI) by using common commercial wireless routers. Compared with previous researches on device-free crowd counting, our proposed method is more adaptive to the change of environment and can achieve high accuracy of crowd count estimation. Because the distance between access point (AP) and monitor point (MP) is typically non-fixed in real-world applications, the strength of received signals varies and makes the traditional amplitude-related models to perform poorly in different environments. In order to achieve adaptivity of the crowd count estimation model, we used convolutional neural network (ConvNet) to extract features from correlation coefficient matrix of subcarriers which are insensitive to the change of received signal strength. We conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79% in estimating a variable number of participants.
Highlights
Wi-Fi has gained an increasing interest in research due to the implementation of orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) technology
In telecommunication with high throughput and multiantenna, the channel state information (CSI) can make the transmissions adapt to current channel condition, which is of great significance
We introduce an adaptive model for human crowd count estimation by exploiting rich CSI data embedded in 802.11n Wi-Fi networks
Summary
Wi-Fi has gained an increasing interest in research due to the implementation of orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) technology. Many researchers have paid much attention on human crowd counting based on the widely deployed wireless routers in public areas. Image-based methods are most often used to estimate the human crowd count, but they are limited to the illumination intensity of environment, line-of-sight propagation property of light, and the public consideration of privacy [9,10,11,12,13,14,15,16,17,18]. We introduce an adaptive model for human crowd count estimation by exploiting rich CSI data embedded in 802.11n Wi-Fi networks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.