CM-NET: Cross-Modal Learning Network for CSI-Based Indoor People Counting in Internet of Things

Abstract

In recent years, multiple IoT solutions have used computational intelligence technologies to identify people and count them. WIFI Channel State Information (CSI) has recently been applied to counting people with multiple benefits, such as being cost-effective, easily accessible, free of privacy concerns, etc. However, most current CSI-based work is limited to human location-fixed environments since human location-random environments are more complicated. Aiming to fix the problem of counting people in human location-random environments, we propose a solution using deep learning CM-NET, an end-to-end cross-modal learning network. Since it is difficult to count people with CSI straightforwardly, CM-NET approaches this problem using deep learning, utilizing a multi-layer transformer model to automatically extract the correlations between channels and the number of people. Owing to the complexity of human location-random environments, the transformer model cannot extract characteristics describing the number of people. To enhance the feature learning capability of the transformer model, CM-NET takes the feature knowledge learned by the image-based people counting model to supervise the learning process. In particular, CM-NET works with CSI alone during the testing phase without any image information, and ultimately achieves sound results with an average accuracy of 86%. Meanwhile, the superiority of CM-NET has been verified by comparison with the latest available related methods.