  In this paper, we present two video processing techniques for contact-less
estimation of the Respiratory Rate (RR) of framed subjects. Due to the modest
extent of movements related to respiration in both infants and adults, specific
algorithms to efficiently detect breathing are needed. For this reason,
motion-related variations in video signals are exploited to identify
respiration of the monitored patient and simultaneously estimate the RR over
time. Our estimation methods rely on two motion magnification algorithms that
are exploited to enhance the subtle respiration-related movements. In
particular, amplitude- and phase-based algorithms for motion magnification are
considered to extract reliable motion signals. The proposed estimation systems
perform both spatial decomposition of the video frames combined with proper
temporal filtering to extract breathing information. After periodic (or
quasi-periodic) respiratory signals are extracted and jointly analysed, we
apply the Maximum Likelihood (ML) criterion to estimate the fundamental
frequency, corresponding to the RR. The performance of the presented methods is
first assessed by comparison with reference data. Videos framing different
subjects, i.e., newborns and adults, are tested. Finally, the RR estimation
accuracy of both methods is measured in terms of normalized Root Mean Squared
Error (RMSE).
