| Title |
Occupancy Detection And People Counting Using Wifi Passive Radar |
| ID_Doc |
39650 |
| Authors |
Tang C.; Li W.; Vishwakarma S.; Chetty K.; Julier S.; Woodbridge K. |
| Year |
2020 |
| Published |
IEEE National Radar Conference - Proceedings, 2020-September |
| DOI |
http://dx.doi.org/10.1109/RadarConf2043947.2020.9266493 |
| Abstract |
Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements. © 2020 IEEE. |
| Author Keywords |
CNN; Crowd Counting; Occupancy Detection; Passive WiFi Radar; WiFi Sensing |
