The deep learning research is more and more focused on the development of specific application that are both effective and efficient, together with the software progress also the hardware research is starting to aim at building embedded devices able to run deep neural networks without using big servers. In this perspective this thesis, developed in Addfor, is aimed at building a software for people counting on an embedded device. The motivation for this choice is to obtain an autonomous and versatile software avoiding all the privacy issues. Regarding the hardware choice, two main edge devices has been considered, the Google Coral dev board and the NVIDIA Jetson nano, they are compared in terms of inference speed and flexibility using a lot of different neural networks.