Abstract: Crowd counting is the process of monitoring the number of people in a certain area,which is crucial in traffic supervision,etc.For example,counting people waiting in lines at airports or retail stores could be used for improving the service.At present,some methods based on videos (or images) and wearable devices have been proposed,but there are some shortcomings in these schemes.For example,the camera can only monitor within the range of sight distance,and wearable devices need people to wear them consciously.Some scholars have made use of radar related technology torealize the number,but its cost is very high.In this paper,an indoor crowd counting scheme,WiCount,based on WiFi signals was proposed.WiCount aims at a fine-grained indoor people counting scheme,which can accurately identify the number of people at different positions.According to the relationship between the number of indoor people and the amplitudes fluctuation of CSI,features are extracted,which are contributed to mitigate the difference of CSI data produced by the same number of people in distinct positions,and then three classifiers (SVM,KNN,BP Neural Network) are trained to identify the number of people in the monitoring area.Prototype systems is implemented in a laboratory and a meeting room respectively,and the recognition is fine when the number of people is on the small side.In the laboratory,the accuracy is up to 90% in the case of no more than 4 persons.In the meeting room,the results show that no matter where people move,the accuracy can reach 89.58% in the case of no more than 2 persons.

Key words: Channel state information, Crowd counting, Machine learning, WiFi sensing, Wireless sensing