HHT-based remote respiratory rate estimation in thermal images

Thermal image has many applications on image processing such as human detection, face recognition and physiological signal evaluation, etc. The respiratory rate is an important physiological signal, and it is highly related to emotion and some diseases. Therefore, we propose a non-contact method to estimate the respiratory rate from thermal image in this paper. Thermal image can provide the information about temperature distribution, so we can use this property to evaluate the breath signal on it. In this work, we use the visual image to detect the face and nose region to locate the ROI for breath signal extraction, then use the histogram equalization to enhance the contrast of thermal image. To make the breath signal more obvious, we proposed a method to find a mask from ROI with Otsu threshold method among some accumulated frames. In addition, Kanade-Lucas-Tomasi (KLT) tracking method is employed to tack the subject to prevent the ROI location error. Next, breath signal is decomposed by Hilbert-Huang Transform (HHT) into some intrinsic mode functions (IMFs). Finally, respiratory rate is estimated by counting the approximate zero-crossing points which are correlated to breathe cycle.