Abstract
Using passive infrared sensors is a well-established technique of presence monitoring. While it can significantly reduce energy consumption, more savings can be made when utilising more modern sensor solutions coupled with machine learning algorithms. This paper proposes an improved method of presence monitoring, which can accurately derive the number of people in the area supervised with a low-cost and low-energy thermal imaging sensor. The method utilises U-Net-like convolutional neural network architecture and has a low parameter count, and therefore can be used in embedded scenarios. Instead of providing simple, binary information, it learns to estimate the occupancy density function with the person count and approximate location, allowing the system to become considerably more flexible. The tests show that the method compares favourably to the state of the art solutions, achieving significantly better results.
Highlights
Residential and industrial buildings are responsible for a substantial portion of overall energy consumption across the world, with a significant share of the energy being used by the heating, ventilation, and air conditioning (HVAC) [1,2,3]
The density function components have a lower value for persons that are only partially visible. This is due to the neural network being less confident of the partially occluded person’s presence, which in turn results in the sum of the distribution components being a fractional number deviating from the true person count
The solution is based on a low-power, low-resolution inexpensive thermal camera
Summary
Residential and industrial buildings are responsible for a substantial portion of overall energy consumption across the world, with a significant share of the energy being used by the heating, ventilation, and air conditioning (HVAC) [1,2,3]. The consequence of this fact is the drive to minimise energy consumption, as this brings economic and environmental benefits. This is especially important in light of the fact that building energy consumption is one of the main driving factors behind carbon dioxide emissions [4]. The most common means for the reduction in HVAC-related energy costs include careful building design or modernisation (e.g., improving thermal insulation provided by walls and windows), enabling heat exchange, ensuring proper ventilation, using modern, efficient HVAC equipment, and, last but not least, intelligent control
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