DeepCount: Crowd Counting with Wi-Fi using Deep Learning

Yanchao Zhao, Shangqing Liu, Fanggang Xue, Bing Chen, Xiang Chen

Abstract: The ubiquitous Wi-Fi devices and recent research efforts on wireless sensing have led to intelligent environments which can sense people's locations and activities in a device-free manner. However, current works are mostly designed for single human environment owing to the complexity of multiple human environments, the limited bandwidth of Wi-Fi and in turn, greatly hinder this technology from the real implementation. To realize such device-free sensing in multiple human environments, the first step-stone is to estimate how many targets or in other words crowd counting in a closed environment, which is not only the basis for multiple human environmental sensing but also leads to many potential applications such as crowd control. To this end, we propose DeepCount—a solution using deep learning approach to infer the number of people in an indoor environment with Wi-Fi signals. Our scheme is based on the key intuition that, although with great complexity, the deep learning approaches can somehow be able to build a complex function to fit the correlation between the number of people and channel state information values. Furthermore, to alleviate the inadequate amount of data required and improve the adaptability of the deep learning approach, we add an online transfer learning approach, which utilizes the entering/leaving results to fine-tune the deep learning model. The prototype of Deep-Count is implemented and evaluated on the commercial Wi-Fi device. By the massive training samples, our deep learning model is able to estimate the number of crowd up to 5 with the mean accuracy of 82.3% by this end-to-end learning approach. Meanwhile, by using the amendment mechanism of the activity recognition model to judge door switch to get the variance of the crowd to amend deep learning predicted results, the accuracy is up to 87% in a rather effective manner.