| Respiration detection technology is divided into contact type and contactless type.For some special patients,the contact type detection operation is inconvenient.Therefore,the respiration detection of contactless type,especially the thermal imaging camera respiration detection,which is a very valuable detection method to perform respiration detection on some patients who are inconvenient to move in clinical practice because of it’s untouchability and simplicity of operator.Consequently,there is currently the most research on it.However,there is no mature implementation plan to detect by infrared thermal imaging cameras with lower resolution.This article uses infrared thermal imaging camera to collect thermal images,and uses the characteristics of airflow changes in the nostrils as the research object to measure the breathing of the nose.The main research contents and results are as follows:In terms of nose recognition,since the target image is an infrared thermal image,the contour and detail information in the image is weaker than the visible light image,also the structural information of the nose part cannot be clearly observed in the thermal image.Therefore,this article has made a preprocessing for the image.Such as denoising,histogram equalization,image sharpening,these preprocessing operations effectively increase the outline and detail information of the image.And in the subsequent processing,using the temperature characteristics of the face part and the nose edge feature,combined with the multi-Otsu method and the Canny edge detection algorithm,the facial image is processed accordingly.The multi-Otsu method can effectively separate the face part from the background part,the contour edge of the nose is found through Canny edge detection,and the automatic recognition of the nose part is completed.In terms of target tracking,the tracking target in this article is the nose area in the infrared thermal image.The characteristics of this area are that the features are poor and contain noise.This article uses a particle filter tracking algorithm based on sparse solution.Aiming at the inconspicuous characteristics of infrared thermal image,the candidate targets are sparsely represented by target template and patch template.The patch template effectively solves the problems of noise and low resolution in the tracking target,and the sparse solution solves the problem that the tracking target features are not obvious.It was verified with experimental subjects,and the average similarity between the following tracking target and the first frame target was above0.998,and the tracking effect was perfect.In terms of signal extraction,the breathing signal extracted from the tracking target contains a larger noise signal.In order to improve the signal-noise ratio,the two schemes are combined.First,the improved frame difference method was used to identify the nostril region,and the average gray value of the region was calculated.Compared with the average gray value of the nose area,the obtained waveform has a significant reduction in waveform noise.The average gray value waveform of the nostril area was filtered,and the differences between several filters were compared.By comparing the mean square error and the signal-noise ratio,the elliptic filter was selected for filtering,and the experimental set was used to verify the filter.The average signal-noise ratio is 5.58 d B,and the average mean square error is 0.245. |
