Convolutional Neural Network on Embedded Platform for People Presence Detection in Low Resolution Thermal Images

Detection of human presence is a key feature in Human Computer Interaction. Solutions based on cameras are attractive, but require computer vision techniques to extract meaningful data, which can be expensive from a computational point of view. In this work, we present a new system that merges a low resolution thermal camera with advanced feature extraction techniques such as Convolutional Neural Networks. We demonstrate the possibility to adapt their execution to resource-constrained platform without significant loss of performance, by processing data on a 32-bit low power microcontroller, performing the classification on thermal video stream. It achieve 76.7% of accuracy in the microcontroller, requiring only 16.5 mW in continuous classification mode and using 6 kB of RAM.