A Lightweight Approach for Passive Human Localization Using an Infrared Thermal Camera

In this article, we study the problem of passive human localization using an infrared (IR) thermal imaging camera which detects IR radiation emitted by human without carry-on devices and thereby generates a heat map of human body. Rather than directly using the heat map, we propose to exploit temperature of human body and design a lightweight approach for human localization using machine learning techniques. We observe that person-to-camera distance is closely related with the position and the size of a person’s head in the heat map, and several other features, such as variance, skewness, and kurtosis of temperatures in the head region are also good indicators of person-to-camera distance estimation. Accordingly, we propose a set of features and construct a model for inferring person-to-camera distance using machine learning techniques. With the estimated distance, we further compute human localization based on the relative position of the person in the heat map, the estimated person-to-camera distance, and the location and the DFoV of the IR thermal camera. Our experiments in real environments show that the proposed approach can accurately estimate person-to-camera distance and human localization with submeter errors.