Thermal image-based CNN's for ultra-low power people recognition
Andres F. Gomez, Francesco Conti, Luca Benini · 2018
Detecting the amount of people occupying an environment is an important use case for surveillance in public spaces such as airports, stations and squares, but also for smaller environments such as classrooms (e.g. to track occupation of classrooms). Using visible imaging for this task is often suboptimal because 1) it potentially violates user privacy 2) to have a good final count, high resolution cameras are required. Long-wave infrared imaging is a viable solution to both these issues. In this paper, we developed a people counting algorithm on thermal images based on convolutional neural networks (CNNs) small enough that they can run on a limited-memory low-power platform. We created a dataset with 3k manually tagged thermal images and developed a fast and accurate CNN that is able to provide a completely error-free detection on 53.7% of the test images and an error bound within ±1 detection in 84.4% of the images, using only 308 kilobytes of system memory in a Cortex M4 platform.