  In intelligent building management, knowing the number of people and their
location in a room are important for better control of its illumination,
ventilation, and heating with reduced costs and improved comfort. This is
typically achieved by detecting people using compact embedded devices that are
installed on the room's ceiling, and that integrate low-resolution infrared
camera, which conceals each person's identity. However, for accurate detection,
state-of-the-art deep learning models still require supervised training using a
large annotated dataset of images. In this paper, we investigate cost-effective
methods that are suitable for person detection based on low-resolution infrared
images. Results indicate that for such images, we can reduce the amount of
supervision and computation, while still achieving a high level of detection
accuracy. Going from single-shot detectors that require bounding box
annotations of each person in an image, to auto-encoders that only rely on
unlabelled images that do not contain people, allows for considerable savings
in terms of annotation costs, and for models with lower computational costs. We
validate these experimental findings on two challenging top-view datasets with
low-resolution infrared images.
