Edge Computing with Embedded AI

Abstract

With the rise of the IoT, there has been a growing demand for people counting and occupancy estimation in Intelligent buildings for adapting their heating, ventilation and cooling systems. This can have a significant impact on energy consumption at a global scale as such systems consume about 40% of electricity and create about 36% of the CO2 emissions in Europe. Previous approaches to occupancy estimation either utilize methods that do not ensure people's privacy when obtaining high accuracy estimations, such as RGB cameras, or utilize thermal or radar sensors with lower accuracy. Thermal vision for people detection has several advantages. It protects people's privacy while being less affected by changes in the environment. In addition, most of the previous image processing approaches rely on streaming the data to the cloud to be analyzed. However, with the development of the more distributed network paradigms edge and fog computing, there has been a trend in moving computation towards the edge of the network. This process of embedding intelligence into end-devices enables more efficient energy consumption and network load distribution. In this work, we present an embedded algorithm for room occupancy estimation based on a thermal sensor with accuracy over the state-of-the-art. We study the performance of a variety of deep learning models on different embedded processors. We achieve a prediction accuracy of 98.9% for people counting estimation with minimal 2 KB RAM utilization. Furthermore, the proposed algorithm has very low latency achieving execution times under 14 ms.