TY - GEN
T1 - Wi-count
T2 - 27th International Conference on Computer Communications and Networks, ICCCN 2018
AU - Yang, Yanni
AU - Cao, Jiannong
AU - Liu, Xuefeng
AU - Liu, Xiulong
PY - 2018/10/9
Y1 - 2018/10/9
N2 - 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.
AB - 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.
KW - People counting
KW - Phase information
KW - WiFi device
UR - http://www.scopus.com/inward/record.url?scp=85060450682&partnerID=8YFLogxK
U2 - 10.1109/ICCCN.2018.8487420
DO - 10.1109/ICCCN.2018.8487420
M3 - Conference article published in proceeding or book
AN - SCOPUS:85060450682
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2018 - 27th International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 July 2018 through 2 August 2018
ER -