Wi-Count : passing people counting with COTS WiFi devices

Abstract

People counting provides valuable information on population mobility and human dynamics, which plays a critical role for intelligent crowd control and retail management. Recently, people counting has been achieved via radio-frequency signals as human presence can influence the propagation of wireless signals, from which the information of the moving crowd can be extracted. However, most of the existing studies using wireless signals only apply to the scenario when people keep moving all the time. Besides, they require labour-intensive training phase for building the counting model. In the Wi-Count system, we take another approach, which is to count the people passing by the doorway with COTS WiFi devices. It can not only detect the passing direction, but also identify the number of people even when multiple persons pass by concurrently without regulating passing behavior and pre-trained counting model. The passing direction is recognized by modeling the effects of the bi-directional passing behavior on the phase difference of WiFi signals. In addition, the number of passing people is obtained through an enhanced signal separation algorithm for providing precise counting result. Extensive experiments show the average accuracy on passing direction detection and passing people counting are about 95% and 92% respectively.