Device-free Pedestrian Count Estimation Using Wi-Fi Channel State Information

Abstract

Dynamic adjustment of signal timing based on the number of pedestrians waiting at a crossing results in safer and more efficient intersections. Traditionally, such information is obtained through push-buttons or vision-based techniques. Though counting is possible with the latter, the techniques are associated with privacy concerns, are affected by poor lighting, and equipment is costly and bulky. We present a novel alternative that uses Wi-Fi Channel State Information (CSI) processed through a Deep Neural Network (DNN) model. The amplitude and phase of 51 Orthogonal Frequency Division Multiplexing sub carriers in the IEEE802.11n standard are captured with two ESP32 devices. Of the candidate DNNs trained with the CSI data, a ID-Convolutional Neural Network is experimentally demonstrated to count up to 12 pedestrians with an accuracy of 79 % at the current level of training. This can be generalized to perform robustly across different environments, and having a footprint of 1.2MB, can be implemented on a Raspberry Pi 4 device. Our contributions include the use of 51 subcarriers, successful outdoor performance, and implementation on low-cost COTS components. It is simple enough to be installed and operated at the roadside, is cost-effective, and is thus scalable to be deployed as a component of Intelligent Transportation Systems.