  The ability to monitor respiratory rate is extremely important for medical
treatment, healthcare and fitness sectors. In many situations, mobile methods,
which allow users to undertake every day activities, are required. However,
current monitoring systems can be obtrusive, requiring users to wear
respiration belts or nasal probes. Recent advances in thermographic systems
have shrunk their size, weight and cost, to the point where it is possible to
create smart-phone based respiration rate monitoring devices that are not
affected by lighting conditions. However, mobile thermal imaging is challenged
in scenes with high thermal dynamic ranges. This challenge is further amplified
by general problems such as motion artifacts and low spatial resolution,
leading to unreliable breathing signals. In this paper, we propose a novel and
robust approach for respiration tracking which compensates for the negative
effects of variations in the ambient temperature and motion artifacts and can
accurately extract breathing rates in highly dynamic thermal scenes. It has
three main contributions. The first is a novel Optimal Quantization technique
which adaptively constructs a color mapping of absolute temperature to improve
segmentation, classification and tracking. The second is the Thermal Gradient
Flow method that computes thermal gradient magnitude maps to enhance accuracy
of the nostril region tracking. Finally, we introduce the Thermal Voxel method
to increase the reliability of the captured respiration signals compared to the
traditional averaging method. We demonstrate the extreme robustness of our
system to track the nostril-region and measure the respiratory rate in high
dynamic range scenes.
