Outdoor People Detection in Low Resolution Thermal Images

Presence detection is a main functionality to make our living spaces smarter and is implemented through several kinds of sensors and smart devices. Recent advancements in embedded systems market and technology enable the design of sophisticated solutions in a low-cost and scalable fashion. However, applications of presence detection, such as surveillance or occupancy detection, home automation or smart lighting are built for indoor scenarios. Therefore, many systems weaken their performance when applied outdoor, where ambient conditions have higher variability. In this work, we describe our exploratory study on people detection in outdoor scenarios by use of an 8x8 pixels resolution thermal sensor. We tested different techniques to extract the presence of a person crossing the detection area. We observed that signal to noise ratio depends on the difference between background and human body temperature. To address this, we collected a dataset spanning a wide range of background conditions and different user clothing and we used it to tune and evaluate the proposed detection techniques. As a possible solution, we propose to adapt the threshold with temperature, providing a regression curve to select it and demonstrate benefits against the use of a fixed threshold with all explored techniques.