Respiratory rate (RR) monitoring is essential in neonatal intensive care units. Despite its importance, RR is still monitored intermittently by manual counting instead of continuous monitoring due to the risk of skin damage with prolonged use of contact electrodes in preterm neonates and false signals due to displacement of electrodes. Thermal imaging has recently gained significance as a non-contact method for RR detection because of its many advantages. However, due to the lack of information in thermal images, the selection and tracking of the region of interest (ROI) in thermal images for neonates are challenging. This paper presents the integration of visible (RGB) and thermal (T) image sequences for the selection and tracking of ROI for breathing rate extraction. The deep-learning based tracking-by-detection approach is employed to detect the ROI in the RGB images, and it is mapped to the thermal images using the RGB-T image registration. The mapped ROI in thermal spectrum sequences gives the respiratory rate. The study was conducted first on healthy adults in different modes, including steady, motion, talking, and variable respiratory order. Subsequently, the method is tested on neonates in a clinical settings. The findings have been validated with a contact-based reference method.The average absolute error between the proposed and belt-based contact method in healthy adults reached 0.1 bpm and for more challenging conditions was approximately 1.5 bpm and 1.8 bpm, respectively. In the case of neonates, the average error is 1.5 bpm, which are promising results. The Bland–Altman analysis showed a good agreement of estimated RR with the reference method RR and this pilot study provided the evidence of using the proposed approach as a contactless method for the respiratory rate detection of neonates in clinical settings.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10877-022-00945-8.