Wi-HFM: A Human Flow Monitoring Method Based on WiFi Channel Features

With the increasing demand for people counting, the technology of human flow monitoring based on channel state information (CSI) attracts much attention because of its advantages such as easy deployment, privacy protection and strong applicability. However, in the existing human flow monitoring work, the accuracy of pedestrian recognition is easily affected by the density of the crowd. To ensure the monitoring accuracy, the monitoring can only be carried out when the crowd is sparse, which leads to the lack of practicability of the human flow monitoring technology based on CSI. In order to solve this problem, a monitoring method that can identify continuous flow of people is proposed. The method firstly uses phase unwrapping and linear phase correction algorithm to eliminate random phase offset and phase compensation for original data, then extracts valid data packets from continuous flow data by standard deviation and variance, and finally inputs phase difference information in the time domain as feature signals into the deep learning Convolutional, Long Short-Term Memory, Deep Neural Network (CLDNN) for pedestrian recognition. After actual testing, the method has achieved outdoor accuracy of 96.7% and indoor accuracy of 94.1% under the condition that the distance between pedestrians in front and back is not less than 1m, outperforming the existing method of human flow monitoring.