Thermo Presence: The Low-resolution Thermal Image Dataset and Occupancy Detection UsingEdge Devices
[ 1 ] Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doctoral school student
[2.2] Automation, electronics, electrical engineering and space technology
2022
chapter in monograph / paper
english
- Thermal Imaging
- Deep Learning
- Edge AI
EN Presence monitoring in office buildings is a vivid topic in building management systems. One of the well-established techniques to achieve it is using infrared sensors. In this paper, we present an annotated dataset consisting of low-resolution thermal images from different office rooms, with a changing number of persons in the scene. For each thermal image, a corresponding image from the RGB camera is available for visual inspection. On each thermal image, the centre position of every person is annotated, allowing not only to know the total number of people but also to track their positions. Along with the dataset, an evaluation of U-Net like convolution neural network architecture on low-power edge devices was carried out, with a comparison of their performance and energy consumption. Due to FLASH memory deficiencies on embedded systems, quantization of the models was applied, with an added benefit of shorter interference time. The presented solution allows estimating the presence density map while maintaining low-level power consumption.
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