Detection of Suspicious Objects Concealed by Walking Pedestrians Using WiFi

Security is of vital importance in public places. Detection of suspicious objects such as metal and liquid often requires dedicated and expensive equipment, preventing its wide deployment. This paper proposes a pervasive device-free method to detect suspicious objects concealed by walking pedestrians using WiFi Channel State Information (CSI). By analyzing the different variations of subcarrier amplitude caused by different materials, the proposed method is able to detect suspicious objects such as metal and liquid concealed by pedestrians, when they walk through the transmission link of the WiFi transmitter and receiver. The proposed method employs Convolutional Neural Network (CNN) to classify suspicious objects, on which majority voting is applied to vote for the final result, in order to improve the detection accuracy for walking pedestrians. Evaluations show that the proposed method with majority voting achieve the detection accuracy of 93.3% for metal and liquid concealed by walking pedestrians, 95.6% for exposed metal and liquid carried by walking pedestrians, and 100% for metal and liquid carried by standing pedestrians.