CN111839519B - Non-contact respiratory frequency monitoring method and system - Google Patents
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Abstract
The invention provides a non-contact respiratory frequency monitoring method and a non-contact respiratory frequency monitoring system, and relates to the field of respiratory frequency monitoring. The method comprises the steps of acquiring a visible light video and a thermal infrared video of a human face, positioning key feature points of the human face from the visible light video, positioning a thermal infrared human face contour from the thermal infrared video, and positioning a nose area in the thermal infrared human face contour based on the key feature points of the human face in the visible light video so as to track the nose area in the thermal infrared video; the nose area in the thermal infrared video is tracked, thermal infrared nose temperature change signals can be obtained, due to the fact that the thermal infrared image gray scale distribution and the target reflection characteristic are in a wireless relation, the thermal infrared nose temperature change signals are hardly affected by the external light change and the difference of different skin colors, and then the thermal infrared nose temperature change signals are preprocessed and calculated, and the respiratory frequency monitoring result with higher accuracy can be obtained.
Description
Technical Field
The invention relates to the technical field of respiratory frequency monitoring, in particular to a non-contact respiratory frequency monitoring method and system.
Background
When the respiratory rate of an infectious patient is monitored, medical staff can be effectively prevented from being infected by a non-contact monitoring method.
The existing non-contact type breathing frequency method is mainly composed of the steps of face monitoring, region of interest (ROI) selection, RGB three-channel color information extraction of the corresponding ROI, waveform signal preprocessing, breathing rate signal extraction and the like. For example, patent document No. 201610404234.1 discloses a non-contact human respiration rate and heart rate synchronous measurement method and system, the measurement method includes the following steps, obtaining human face visible light video, selecting two interested areas from video frame images; respectively using a coherent averaging method to the pixel values of the selected double interested areas of each frame in the video to generate 2 groups of RGB observation signals, and then sequentially carrying out high-pass filtering, trend removing, mean value removing and normalization preprocessing operations on the 2 groups of RGB observation signals; the device is used for carrying out 6-channel blind source separation on 2 groups of RGB observation signals generated based on the double interested areas, and separating out a respiration signal and a heart rate signal; the method is used for identifying a respiration signal and a heart rate signal from source signals after blind source separation, and extracting respiration and heart rate by combining a sliding window algorithm.
However, when the above method is used for collecting a visible light video of a human face, the accuracy of RGB observation signals is reduced by external light changes or individual skin color differences, which further affects the accuracy of a respiratory frequency monitoring result.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a non-contact type respiratory frequency monitoring method, which solves the problem that the accuracy of RGB (red, green and blue) observation signals is reduced due to external light change or personal skin color difference when a visible light video of a human face is collected.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method of contactless respiratory rate monitoring, the method comprising:
s1, collecting visible light videos and thermal infrared videos of the human face;
s2, positioning key feature points of the human face from the visible light video, and positioning thermal infrared human face outlines from the thermal infrared video;
s3, positioning a nose area in the thermal infrared face contour based on key feature points of the face in the visible light video;
s4, tracking a nose area in the thermal infrared face contour in real time, and acquiring a thermal infrared nose temperature change signal in an acquisition time period;
s5, preprocessing the thermal infrared nose temperature change signal;
and S6, acquiring the respiratory frequency based on the preprocessed thermal infrared nose temperature change signal.
Preferably, the visible light video and the thermal infrared video are acquired synchronously in S1, and the acquisition time is one minute.
Preferably, the locating the key feature points of the human face from the visible light video in S2 includes:
and positioning key feature points of the human face from the visible light video through an SURF feature extraction algorithm to obtain coordinates of the key feature points.
Preferably, the locating the thermal infrared face contour from the thermal infrared video in S2 includes:
k1, acquiring a background thermal infrared image P1 before acquiring a thermal infrared video of a human face;
k2, acquiring a pixel coordinate set pos1 of the thermal infrared image P1, wherein the pixel coordinate set pos is higher than a threshold value E;
k3, randomly acquiring a thermal infrared image P2 of the ith frame when a thermal infrared video of the face is acquired;
k4, acquiring a pixel coordinate set pos2 of the thermal infrared image P2, wherein the pixel coordinate set pos is higher than a threshold value E;
k5, acquiring a background thermal infrared image P3 after the thermal infrared video acquisition of the human face is finished;
k6, acquiring a pixel coordinate set pos3 of the thermal infrared image P3, wherein the pixel coordinate set pos is higher than a threshold value E;
k7, screening out a pixel coordinate set pos4 of the intersection part of the pixel coordinate set pos1 and the pixel coordinate set pos 3;
and K8, deleting the pixel coordinate set pos4 from the pixel coordinate set pos2 to obtain a coordinate set pos5 of points corresponding to the thermal infrared face contour.
Preferably, the locating the nose region in the thermal infrared face contour based on the key feature points of the face in the visible light video in S3 includes:
s3-1, obtaining coordinates A, B of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the human face in the visible light video based on the coordinates of all key feature points in the visible light video;
s3-2, obtaining coordinates C, D of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the thermal infrared face contour in the thermal infrared video based on coordinates of points of the thermal infrared face contour;
s3-3, obtaining coordinates of key feature points corresponding to the nose in the visible light video in the thermal infrared video based on the coordinates A, B, C, D and the coordinates of the key feature points corresponding to the nose in the visible light video, and the conversion formulas of the coordinates (x _ vn (i), y _ vn (i)) of the ith key feature point corresponding to the nose in the visible light video and the coordinates (x _ in (i), y _ in (i)) in the corresponding thermal infrared video are as follows:
WIF＝WI，
wherein WV and HV are respectively the width and height of a rectangular frame picture corresponding to a human face in a visible light video, WI and HI are respectively the width and height of a picture of a common area of a thermal infrared human face and a neck, WIF and HIF are the width WIF, height HIF and x of the thermal infrared human face areaminIs the abscissa, y, of the upper left corner AminIs the ordinate of the upper left corner point A;
s3-4, obtaining a nose area in the thermal infrared face contour based on coordinates of key feature points corresponding to the nose in all the visible light videos in the thermal infrared video.
Preferably, the step of tracking the nose region in the thermal infrared face contour in real time in S4, and acquiring the thermal infrared nose temperature variation signal in the collection time period includes:
s4-1, acquiring a nose region coordinate P (t) of a t frame in a thermal infrared video, sampling the P (t), and training a linear regressor for calculating a response of rectangular frame sampling corresponding to a nose region through an Adaboost learning algorithm; wherein the sample is labeled with a succession of labels;
s4-2, sampling P (t) corresponding to the first 3 frames in the t +1 th frame in the thermal infrared video, and acquiring the response of each sampling by using the linear regressor;
s4-3, taking the sample with the strongest response as the nose region coordinate P (t +1) of the t +1 th frame, and calculating the formula as follows: p (t +1) ═ 1/3 (P (t-2) + P (t-1) + P (t));
s4-4, converting the thermal infrared image into a gray image, and acquiring a gray average value Signal _ lose (i) of continuous frames of a nose area in the thermal infrared video as a nose temperature change Signal of an acquisition time period; and the calculation mode of the gray average value of the nose area in the thermal infrared video in the ith frame picture is as follows:
Signal_nose(i)＝mean(Gray)；
Gray＝0.3Rnose+0.59Gnose+0.11Bnose；
wherein Gray is a Gray scale image matrix of the nose region; mean (Gray) is a function of the gray level average of the nose region in thermal infrared video, Rnose、Gnose、BnoseThree-channel image matrices in the nose region R, G, B of the thermal infrared video, respectively.
Preferably, the preprocessing the thermal infrared nose temperature change signal in S5 includes performing trend term elimination, normalization processing, and filtering and denoising processing in sequence.
Preferably, the acquiring the respiratory frequency based on the preprocessed thermal infrared nasal temperature variation signal in S6 includes:
s6-1, converting the thermal infrared nose temperature change signal into a frequency domain signal;
s6-2, reserving frequency domain signals with frequency values within 0.15-0.4Hz, and enabling frequency domain signals outside 0.15-0.4Hz to be zero;
s6-3, obtaining a frequency value corresponding to the maximum amplitude value in the frequency domain signal within the range of 0.15-0.4Hz, and obtaining the respiratory frequency.
A non-contact respiratory frequency monitoring system comprises a double-light video acquisition module, a key characteristic point positioning module, a thermal infrared human face contour positioning module, a double-light registration module, a temperature change signal extraction module, a preprocessing module and a respiratory frequency calculation module;
the double-light video acquisition module is used for acquiring visible light videos and thermal infrared videos of a human face;
the key characteristic point positioning module is used for positioning key characteristic points of the human face from the visible light video;
the slave thermal infrared face contour positioning module is used for positioning a thermal infrared face contour from a thermal infrared video;
the double-light registration module is used for positioning a nose area in the thermal infrared face contour based on key feature points of a face in the visible light video;
the temperature change signal extraction module is used for tracking a nose area in the thermal infrared face contour in real time and acquiring a thermal infrared nose temperature change signal in an acquisition time period;
the preprocessing module is used for preprocessing the thermal infrared nose temperature change signal;
the respiratory frequency calculation module is used for acquiring respiratory frequency based on the preprocessed thermal infrared nose temperature change signal.
(III) advantageous effects
The invention provides a non-contact respiratory frequency monitoring method and a non-contact respiratory frequency monitoring system, which have the following beneficial effects compared with the prior art:
the method comprises the steps of acquiring a visible light video and a thermal infrared video of a human face, positioning key feature points of the human face from the visible light video, positioning a thermal infrared human face contour from the thermal infrared video, and positioning a nose area in the thermal infrared human face contour based on the key feature points of the human face in the visible light video so as to track the nose area in the thermal infrared video; the nose area in the thermal infrared video is tracked, thermal infrared nose temperature change signals can be obtained, due to the fact that the thermal infrared image gray scale distribution and the target reflection characteristic are in a wireless relation, the thermal infrared nose temperature change signals are hardly affected by the external light change and the difference of different skin colors, and then the thermal infrared nose temperature change signals are preprocessed and calculated, and the respiratory frequency monitoring result with higher accuracy can be obtained.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of example 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a non-contact type respiratory frequency monitoring method, so that the problem that the accuracy of RGB observation signals is reduced due to external light change or personal skin color difference when a visible light video of a human face is collected is solved, and the effect of improving the accuracy of a respiratory frequency monitoring result is achieved.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
the method comprises the steps of acquiring a visible light video and a thermal infrared video of a human face, positioning key feature points of the human face from the visible light video, positioning a thermal infrared human face contour from the thermal infrared video, and positioning a nose area in the thermal infrared human face contour based on the key feature points of the human face in the visible light video so as to track the nose area in the thermal infrared video; the nose area in the thermal infrared video is tracked, thermal infrared nose temperature change signals can be obtained, due to the fact that the thermal infrared image gray scale distribution and the target reflection characteristic are in a wireless relation, the thermal infrared nose temperature change signals are hardly affected by the external light change and the difference of different skin colors, and then the thermal infrared nose temperature change signals are preprocessed and calculated, and the respiratory frequency monitoring result with higher accuracy can be obtained.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
a method of contactless respiratory rate monitoring, the method being performed by a computer, the method comprising S1-S6:
s1, collecting visible light videos and thermal infrared videos of the human face;
s2, positioning key feature points of the human face from the visible light video, and positioning thermal infrared human face outlines from the thermal infrared video;
s3, positioning a nose area in the thermal infrared face contour based on key feature points of the face in the visible light video;
s4, tracking a nose area in the thermal infrared face contour in real time, and acquiring a thermal infrared nose temperature change signal in an acquisition time period;
s5, preprocessing the thermal infrared nose temperature change signal;
and S6, acquiring the respiratory frequency based on the preprocessed thermal infrared nose temperature change signal.
Compared with the prior art, the method and the device have the advantages that the visible light video and the thermal infrared video of the human face are collected, the key characteristic points of the human face are located from the visible light video, the thermal infrared human face contour is located from the thermal infrared video, and the nose area in the thermal infrared human face contour is located based on the key characteristic points of the human face in the visible light video, so that the nose area can be tracked in the thermal infrared video; the nose area in the thermal infrared video is tracked, thermal infrared nose temperature change signals can be obtained, due to the fact that the thermal infrared image gray scale distribution and the target reflection characteristic are in a wireless relation, the thermal infrared nose temperature change signals are hardly affected by the external light change and the difference of different skin colors, and then the thermal infrared nose temperature change signals are preprocessed and calculated, and the respiratory frequency monitoring result with higher accuracy can be obtained.
In this embodiment, in S1, the visible light video and the thermal infrared video are captured synchronously, and the capturing time is one minute.
In this embodiment, the locating the key feature points of the human face from the visible light video in S2 includes:
and positioning key feature points of the human face from the visible light video through an SURF feature extraction algorithm to obtain coordinates of the key feature points.
In this embodiment, the locating the thermal infrared face contour from the thermal infrared video in S2 includes:
k1, acquiring a background thermal infrared image P1 before acquiring a thermal infrared video of a human face;
k2, acquiring a pixel coordinate set pos1 of the thermal infrared image P1, wherein the pixel coordinate set pos is higher than a threshold value E;
k3, randomly acquiring a thermal infrared image P2 of the ith frame when a thermal infrared video of the face is acquired;
k4, acquiring a pixel coordinate set pos2 of the thermal infrared image P2, wherein the pixel coordinate set pos is higher than a threshold value E;
k5, acquiring a background thermal infrared image P3 after the thermal infrared video acquisition of the human face is finished;
k6, acquiring a pixel coordinate set pos3 of the thermal infrared image P3, wherein the pixel coordinate set pos is higher than a threshold value E;
k7, screening out a pixel coordinate set pos4 of the intersection part of the pixel coordinate set pos1 and the pixel coordinate set pos 3;
and K8, deleting the pixel coordinate set pos4 from the pixel coordinate set pos2 to obtain a coordinate set pos5 of points corresponding to the thermal infrared face contour.
In this embodiment, the locating the nose region in the thermal infrared face contour based on the key feature points of the face in the visible light video in S3 includes:
s3-1, obtaining coordinates A, B of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the human face in the visible light video based on the coordinates of all key feature points in the visible light video;
s3-2, obtaining coordinates C, D of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the thermal infrared face contour in the thermal infrared video based on coordinates of points of the thermal infrared face contour;
s3-3, obtaining coordinates of key feature points corresponding to the nose in the visible light video in the thermal infrared video based on the coordinates A, B, C, D and the coordinates of the key feature points corresponding to the nose in the visible light video, and the conversion formulas of the coordinates (x _ vn (i), y _ vn (i)) of the ith key feature point corresponding to the nose in the visible light video and the coordinates (x _ in (i), y _ in (i)) in the corresponding thermal infrared video are as follows:
WIF＝WI，
wherein WV and HV are respectively the width and height of a rectangular frame picture corresponding to a human face in a visible light video, WI and HI are respectively the width and height of a picture of a common area of a thermal infrared human face and a neck, WIF and HIF are the width WIF, height HIF and x of the thermal infrared human face areaminIs the abscissa, y, of the upper left corner AminIs the ordinate of the upper left corner point A;
s3-4, obtaining a nose area in the thermal infrared face contour based on coordinates of key feature points corresponding to the nose in all the visible light videos in the thermal infrared video.
In this embodiment, the step of tracking the nose region in the thermal infrared face contour in real time in S4, and acquiring the thermal infrared nose temperature variation signal in the collection time period includes:
s4-1, acquiring a nose region coordinate P (t) of a t frame in a thermal infrared video, sampling the P (t), and training a linear regressor for calculating a response of rectangular frame sampling corresponding to a nose region through an Adaboost learning algorithm; wherein the sample is labeled with a succession of labels;
s4-2, sampling P (t) corresponding to the first 3 frames in the t +1 th frame in the thermal infrared video, and acquiring the response of each sampling by using the linear regressor;
s4-3, taking the sample with the strongest response as the nose region coordinate P (t +1) of the t +1 th frame, and calculating the formula as follows: p (t +1) ═ 1/3 (P (t-2) + P (t-1) + P (t));
s4-4, converting the thermal infrared image into a gray image, and acquiring a gray average value Signal _ lose (i) of continuous frames of a nose area in the thermal infrared video as a nose temperature change Signal of an acquisition time period; and the calculation mode of the gray average value of the nose area in the thermal infrared video in the ith frame picture is as follows:
Signal_nose(i)＝mean(Gray)；
Gray＝0.3Rnose+0.59Gnose+0.11Bnose；
wherein Gray is a Gray scale image matrix of the nose region; mean (Gray) is a function of the gray level average of the nose region in thermal infrared video, Rnose、Gnose、BnoseThree-channel image matrices in the nose region R, G, B of the thermal infrared video, respectively.
In this embodiment, the preprocessing the thermal infrared nasal temperature variation signal in S5 includes performing trend term elimination, normalization processing, and filtering and denoising processing in sequence.
In this embodiment, the acquiring a respiratory rate based on the preprocessed thermal infrared nasal temperature variation signal in S6 includes:
s6-1, converting the thermal infrared nose temperature change signal into a frequency domain signal;
s6-2, reserving frequency domain signals with frequency values within 0.15-0.4Hz, and enabling frequency domain signals outside 0.15-0.4Hz to be zero;
s6-3, obtaining a frequency value corresponding to the maximum amplitude value in the frequency domain signal within the range of 0.15-0.4Hz, and obtaining the respiratory frequency.
The following describes the implementation process of the present embodiment in detail:
and S1, synchronously acquiring the visible light video and the thermal infrared video of the human face through the double-light video acquisition module, wherein the acquisition time is one minute.
S2, the key feature point positioning module positions key feature points of the human face from the visible light video through an SURF feature extraction algorithm or other feature extraction algorithms to obtain coordinates of the key feature points;
for the key feature points, an Adaboost learning algorithm is adopted, the voting result of each weak classifier is weighted according to a voting mechanism for matrix features (weak classifiers) which can represent the key features of the human face most, so that the matrix features are combined into a strong classifier, a plurality of strong classifiers obtained by training are connected in series to form a cascade-structured cascade classifier, finally, the human face monitoring is realized, and 81 key feature points of the nose, the eyes, the mouth, the chin and the like of the human face in the visible light video are accurately calibrated.
Because the surface temperature of a human body is usually 33.5-37 ℃, a measured person generally carries out indoor acquisition, the indoor environment temperature is generally lower than the surface temperature of the human body, and a human face area and the environment area have a large temperature difference value, according to the characteristic, an area formed by points with temperature values higher than a threshold value E is screened out from a thermal infrared video through a thermal infrared human face contour positioning module to be used as a thermal infrared human face contour, and coordinates of the thermal infrared human face contour are obtained. For example, E is 33.5 ℃. However, in application, there is a local high-temperature region in the acquisition environment, which causes deviation in thermal infrared face contour positioning, and in order to avoid this problem, the following scheme can be adopted to ensure accurate positioning of the thermal infrared face contour.
The positioning of the thermal infrared face contour from the thermal infrared video comprises:
k1, acquiring a background thermal infrared image P1 before acquiring a thermal infrared video of a human face;
k2, acquiring a pixel coordinate set pos1 of the thermal infrared image P1, wherein the pixel coordinate set pos is higher than a threshold value E;
k3, randomly acquiring a thermal infrared image P2 of the ith frame when a thermal infrared video of the face is acquired;
k4, acquiring a pixel coordinate set pos2 of the thermal infrared image P2, wherein the pixel coordinate set pos is higher than a threshold value E;
k5, acquiring a background thermal infrared image P3 after the thermal infrared video acquisition of the human face is finished;
k6, acquiring a pixel coordinate set pos3 of the thermal infrared image P3, wherein the pixel coordinate set pos is higher than a threshold value E;
k7, screening out a pixel coordinate set pos4 of the intersection part of the pixel coordinate set pos1 and the pixel coordinate set pos 3;
and K8, deleting the pixel coordinate set pos4 from the pixel coordinate set pos2 to obtain a coordinate set pos5 of points corresponding to the thermal infrared face contour.
The scheme for acquiring the background thermal infrared image P1 in the K1 is as follows: the thermal infrared video acquisition module keeps video acquisition before acquisition, determines the time t when a person to be detected enters a monitoring area through a sensor, such as a pressure sensor arranged on a seat, and the like, acquires a frame screenshot of a thermal infrared video n seconds before t as P1, and similarly determines the time t' when the person to be detected enters the monitoring area, and acquires a frame screenshot of the thermal infrared video n seconds after t as P3. n may be set to 3 seconds. The high-temperature region in the collection environment can be determined by comparing the screenshot of the thermal infrared video frames of the collection environment before and after collection, and the influence of the local high-temperature region in the collection environment on the positioning accuracy of the thermal infrared face contour can be avoided by removing the existing thermal infrared video from the collected thermal infrared video in the high-temperature region.
S3, the positioning the nose region in the thermal infrared face contour based on the key feature points of the face in the visible light video through the dual-optical registration module includes:
s3-1, obtaining coordinates A, B of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the human face in the visible light video based on the coordinates of all key feature points in the visible light video; the method specifically comprises the following steps: ith key feature point of human faceCan be noted as (x)i,yi) Then, the abscissa set X ═ X of all key feature points can be obtained1,x2,…,xMY and the ordinate set Y ═ Y1,y2,…,yMTherefore, when the point at the upper left corner is taken as the origin of coordinates of the image, the coordinates of the upper left corner point of the rectangular frame corresponding to the face in the visible light video are a (x)min，ymin) The coordinate of the lower right corner point is B (x)max，ymax) (ii) a And X belongs to X, Y belongs to Y, XminIs the minimum of X, XmaxIs the maximum value of X, yminIs the minimum value of Y, YmaxIs the maximum value in Y.
S3-2, obtaining coordinates C, D of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the thermal infrared face contour in the thermal infrared video based on coordinates of points of the thermal infrared face contour; similar to the acquisition method of A, B, for example, when E ═ 33.5 ℃, there are N points in the thermal infrared face contour, and the coordinate of the ith point in the thermal infrared face contour can be recorded as (x ″).i，y”i) The abscissa set X ═ X ″, which can be obtained for all points in the thermal infrared face contour "1,x”2,…,x”NAnd the ordinate set Y ═ Y "1,y”2,…,y”NH, then C (x) "min，y”min)、D(x”max，y”max) And X "∈ X", Y "∈ Y", X "minIs the minimum value of X ', X'maxIs the maximum value of X', y "minIs the minimum value of Y ', Y'maxIs the maximum value in Y ".
Because the sizes of the human faces in the visible light video and the human faces in the thermal infrared video which are divided are different, the feature points can be in one-to-one correspondence only by carrying out uniform conversion.
S3-3, the width and height of the rectangular frame picture corresponding to the human face in the visible light video are respectively WV and HV, the width and height of the picture of the common area of the thermal infrared human face and the neck are WI and HI, and the width WIF and height HIF of the thermal infrared human face area are respectively:
WIF＝WI，
WV and HV can be determined from A, B, and WI and HI can be determined from C, D.
The coordinates of the ith key feature point corresponding to the nose in the visible light video are (x _ vn (i), y _ vn (i)), and the calculation formula corresponding to the coordinates (x _ in (i), y _ in (i)) in the thermal infrared video is as follows:
s3-4, obtaining a nose area in the thermal infrared face contour based on coordinates of key feature points corresponding to the nose in all the visible light videos in the thermal infrared video.
S4, the temperature of the gas near the nose has obvious difference in the breathing process of the person, and the temperature change of the gas near the nose has periodicity. According to the characteristics, tracking a nose region in the thermal infrared face contour through a temperature change Signal extraction module, converting a thermal infrared image into a gray image, and obtaining a gray average value Signal _ lose (i) of continuous frames of the nose region in the thermal infrared video as a nose temperature change Signal of an acquisition time period; however, considering that there may be a problem of tracking error caused by head movement in actual conditions, in order to solve this problem, the following steps may be adopted:
s4-1, acquiring a nose region coordinate P (t) of a t frame in a thermal infrared video, sampling the P (t), and training a linear regressor for calculating a response of rectangular frame sampling corresponding to a nose region through an Adaboost learning algorithm; wherein the sample is labeled with a succession of labels; (i.e., values of the [0,1] range are assigned respectively according to the distance between the center of the sample and the target. the closer the sample is, the closer the value is to 1, the farther the value is toward 0. the nuclear correlation filter algorithm (KCF) gives different weights to the samples obtained at different offsets by using the values of the [0,1] range as the regression values of the samples).
The choice to train a linear regressor is basically to take positive samples at the center of the target and then negative samples based on surrounding images. Most algorithms use a non-positive or negative method to label training samples, i.e. the positive sample label is 1 and the negative sample label is 0. This labeling method has a problem in that the weight of each negative sample is not well reflected, i.e., samples far from the target and samples near to the target are treated the same.
S4-2, sampling P (t) corresponding to the first 3 frames in the t +1 th frame in the thermal infrared video, and acquiring the response of each sampling by using the linear regressor;
s4-3, taking the sample with the strongest response as the nose region coordinate P (t +1) of the t +1 th frame, and calculating the formula as follows: p (t +1) ═ 1/3 (P (t-2) + P (t-1) + P (t)); namely, the coordinates of the picture of the current frame are determined by the average value of the coordinates of the first three frames.
S4-4, converting the thermal infrared image into a gray image, and acquiring a gray average value Signal _ lose (i) of continuous frames of a nose area in the thermal infrared video as a nose temperature change Signal of an acquisition time period; and the calculation mode of the gray average value of the nose area in the thermal infrared video in the ith frame picture is as follows:
Signal_nose(i)＝mean(Gray)；
Gray＝0.3Rnose+0.59Gnose+0.11Bnose；
wherein Gray is a Gray scale image matrix of the nose region; mean (Gray) is a function R of the mean of the gray levels of the nose region in thermal infrared videonose、Gnose、BnoseThree-channel image matrices in the nose region R, G, B of the thermal infrared video, respectively.
S5, because the acquired initial nose temperature change signal vibrates to a certain degree, and the low-frequency component can also influence the nose temperature signal; meanwhile, due to the instability of the signal acquisition device and the extreme susceptibility to interference of the surrounding environment, zero drift of the signal is generated, the signal is deviated from the baseline frequently, and even the deviation of the signal from the baseline is changed along with time. The entire process of time-dependent variation from the baseline is called the trend term of the signal. The trend item can influence the quality and the correctness of the signal, so that the trend item of the nose temperature change signal is eliminated through the preprocessing module; subsequently, the nose temperature change signal is normalized, and the calculation formula is as follows:
where σ is a standard deviation, μ is a mean of the original signal, signal _ nose is a nose temperature change signal before normalization, and signal _ nose' is a nose temperature change signal after normalization.
And then, filtering and denoising the normalized nose temperature change signal. Since the waveform of respiration is relatively smooth, a low frequency signal corresponds. While the frequency of the noise is higher. If the original signal can be decomposed, the low-frequency part is reserved, and the high-frequency part is filtered, the signal denoising can be realized. A Butterworth filter is particularly useful because it has the maximum flat amplitude characteristic in the pass band, and because the amplitude in positive frequencies decreases monotonically with increasing frequency, typically used for low-pass filtering, can filter out signals at normal breathing frequencies. The Butterworth filter is defined as:
wherein omegapAnd omega is the cut-off frequency of the upper limit and the lower limit of the passband, and N is the order of the Butterworth filter. Typically, the maximum attenuation allowed by the passband is chosen to be 3dB, when e is 1.
S6, acquiring the respiratory frequency based on the preprocessed thermal infrared nose temperature change signal through the respiratory frequency calculation module comprises the following steps:
s6-1, converting the thermal infrared nose temperature change signal into a frequency domain signal by using fast Fourier transform;
s6-2, the normal human breathing frequency is 9-24bpm, the corresponding frequency band is 0.15-0.4Hz, therefore, the frequency domain signal with the frequency value within 0.15-0.4Hz is reserved, and the frequency domain signal outside 0.15-0.4Hz is returned to zero; in this way, on the one hand, the noise frequency can be eliminated and, on the other hand, frequency domain information useful for the respiratory frequency analysis can be extracted.
S6-3, obtaining a frequency value f corresponding to the maximum amplitude value in the frequency domain signal within the range of 0.15-0.4HzmaxObtaining the respiratory rate RR, wherein the calculation formula of the respiratory rate RR is RR ═ fmax*60。
In summary, compared with the prior art, the embodiment of the invention has the following beneficial effects:
1. the method comprises the steps of collecting a visible light video and a thermal infrared video of a human face, positioning key feature points of the human face from the visible light video, positioning a thermal infrared human face contour from the thermal infrared video, and positioning a nose area in the thermal infrared human face contour based on the key feature points of the human face in the visible light video so as to track the nose area in the thermal infrared video; the nose area in the thermal infrared video is tracked, thermal infrared nose temperature change signals can be obtained, due to the fact that the thermal infrared image gray scale distribution and the target reflection characteristic are in a wireless relation, the thermal infrared nose temperature change signals are hardly affected by the external light change and the difference of different skin colors, and then the thermal infrared nose temperature change signals are preprocessed and calculated, and the respiratory frequency monitoring result with higher accuracy can be obtained.
2. By keeping the video collection before the collection, determining the time t when the person to be detected enters the monitoring area through a sensor, such as a pressure sensor arranged on a seat, and the like, acquiring the frame screenshot of the thermal infrared video n seconds before t as P1, and similarly, determining the time t' when the person to be detected enters the monitoring area, and acquiring the frame screenshot of the thermal infrared video n seconds after t as P3. n may be set to 3 seconds. The high-temperature region in the collection environment can be determined by comparing the screenshot of the thermal infrared video frames of the collection environment before and after collection, and the influence of the local high-temperature region in the collection environment on the positioning accuracy of the thermal infrared face contour can be avoided by removing the existing thermal infrared video from the collected thermal infrared video in the high-temperature region.
3. The tracking algorithm provided by the embodiment of the invention can well reflect the weight of each negative sample during face tracking, so that the face can be accurately tracked, and the problem of tracking error caused by head movement is solved.
Example 2
The invention also provides a non-contact respiratory frequency monitoring system, which comprises a double-light video acquisition module, a key characteristic point positioning module, a thermal infrared human face contour positioning module, a double-light registration module, a temperature change signal extraction module, a preprocessing module and a respiratory frequency calculation module;
the double-light video acquisition module is used for acquiring visible light videos and thermal infrared videos of a human face;
the key characteristic point positioning module is used for positioning key characteristic points of the human face from the visible light video;
the slave thermal infrared face contour positioning module is used for positioning a thermal infrared face contour from a thermal infrared video;
the double-light registration module is used for positioning a nose area in the thermal infrared face contour based on key feature points of a face in the visible light video;
the temperature change signal extraction module is used for tracking a nose area in the thermal infrared face contour in real time and acquiring a thermal infrared nose temperature change signal in an acquisition time period;
the preprocessing module is used for preprocessing the thermal infrared nose temperature change signal;
the respiratory frequency calculation module is used for acquiring respiratory frequency based on the preprocessed thermal infrared nose temperature change signal.
It can be understood that, the non-contact respiratory rate monitoring system provided in the embodiment of the present invention corresponds to the non-contact respiratory rate monitoring method, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the non-contact respiratory rate monitoring method, which are not described herein again.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method of contactless respiratory rate monitoring, the method comprising:
s1, collecting visible light videos and thermal infrared videos of the human face;
s2, positioning key feature points of the human face from the visible light video, and positioning thermal infrared human face outlines from the thermal infrared video;
s3, positioning a nose area in the thermal infrared face contour based on key feature points of the face in the visible light video;
s4, tracking a nose area in the thermal infrared face contour in real time, and acquiring a thermal infrared nose temperature change signal in an acquisition time period; the method comprises the following steps:
s4-1, acquiring a nose region coordinate P (t) of a t frame in a thermal infrared video, sampling the P (t), and training a linear regressor for calculating a response of rectangular frame sampling corresponding to a nose region through an Adaboost learning algorithm; wherein the sample is labeled with a succession of labels;
s4-2, sampling P (t) corresponding to the first 3 frames in the t +1 th frame in the thermal infrared video, and acquiring the response of each sampling by using the linear regressor;
s4-3, taking the sample with the strongest response as the nose region coordinate P (t +1) of the t +1 th frame, and calculating the formula as follows: p (t +1) ═ 1/3 (P (t-2) + P (t-1) + P (t));
s4-4, converting the thermal infrared image into a gray image, and acquiring a gray average value Signal _ lose (i) of continuous frames of a nose area in the thermal infrared video as a nose temperature change Signal of an acquisition time period; and the calculation mode of the gray average value of the nose area in the thermal infrared video in the ith frame picture is as follows:
Signal_nose(i)＝mean(Gray)；
Gray＝0.3Rnose+0.59Gnose+0.11Bnose；
wherein Gray is a Gray scale image matrix of the nose region; mean (Gray) is a function of the gray level average of the nose region in thermal infrared video, Rnose、Gnose、BnoseThree-channel image matrixes of a nose area R, G, B in the thermal infrared video respectively;
s5, preprocessing the thermal infrared nose temperature change signal;
and S6, acquiring the respiratory frequency based on the preprocessed thermal infrared nose temperature change signal.
2. The method of claim 1, wherein the visible light video and the thermal infrared video are captured simultaneously in S1, and the capturing time is one minute.
3. The method for contactless respiratory rate monitoring according to claim 1, wherein the locating the key feature points of the human face from the visible light video in S2 includes:
and positioning key feature points of the human face from the visible light video through an SURF feature extraction algorithm to obtain coordinates of the key feature points.
4. The method according to claim 3, wherein the locating the thermal infrared face contour from the thermal infrared video in S2 comprises:
k1, acquiring a background thermal infrared image P1 before acquiring a thermal infrared video of a human face;
k2, acquiring a pixel coordinate set pos1 of the thermal infrared image P1, wherein the pixel coordinate set pos is higher than a threshold value E, and the threshold value E is a human body surface temperature threshold value;
k3, randomly acquiring a thermal infrared image P2 of the ith frame when a thermal infrared video of the face is acquired;
k4, acquiring a pixel coordinate set pos2 of the thermal infrared image P2, wherein the pixel coordinate set pos is higher than a threshold value E;
k5, acquiring a background thermal infrared image P3 after the thermal infrared video acquisition of the human face is finished;
k6, acquiring a pixel coordinate set pos3 of the thermal infrared image P3, wherein the pixel coordinate set pos is higher than a threshold value E;
k7, screening out a pixel coordinate set pos4 of the intersection part of the pixel coordinate set pos1 and the pixel coordinate set pos 3;
and K8, deleting the pixel coordinate set pos4 from the pixel coordinate set pos2 to obtain a coordinate set pos5 of points corresponding to the thermal infrared face contour.
5. The method of claim 4, wherein the step of locating the nose region in the thermal infrared face contour based on the key feature points of the face in the visible light video in S3 comprises:
s3-1, obtaining coordinates A, B of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the human face in the visible light video based on the coordinates of all key feature points in the visible light video;
s3-2, obtaining coordinates C, D of an upper left corner point and a lower right corner point of a rectangular frame corresponding to the thermal infrared face contour in the thermal infrared video based on coordinates of points of the thermal infrared face contour;
s3-3, obtaining coordinates of key feature points corresponding to the nose in the visible light video in the thermal infrared video based on the coordinates A, B, C, D and the coordinates of the key feature points corresponding to the nose in the visible light video, and the conversion formulas of the coordinates (x _ vn (i), y _ vn (i)) of the ith key feature point corresponding to the nose in the visible light video and the coordinates (x _ in (i), y _ in (i)) in the corresponding thermal infrared video are as follows:
WIF＝WI，
wherein WV and HV are respectively the width and height of a rectangular frame picture corresponding to a human face in a visible light video, WI and HI are respectively the width and height of a picture of a common area of a thermal infrared human face and a neck, WIF and HIF are the width WIF, height HIF and x of the thermal infrared human face areaminIs the abscissa, y, of the upper left corner AminIs the ordinate of the upper left corner point A;
s3-4, obtaining a nose area in the thermal infrared face contour based on coordinates of key feature points corresponding to the nose in all the visible light videos in the thermal infrared video.
6. The method for non-contact respiratory frequency monitoring according to claim 1, wherein the preprocessing of the thermal infrared nasal temperature variation signal in S5 includes trend term elimination, normalization processing, and filtering and de-noising processing in sequence.
7. The method for non-contact respiratory rate monitoring according to claim 1, wherein the step of obtaining respiratory rate based on the preprocessed thermal infrared nasal temperature variation signal in S6 comprises:
s6-1, converting the thermal infrared nose temperature change signal into a frequency domain signal;
s6-2, reserving frequency domain signals with frequency values within 0.15-0.4Hz, and enabling frequency domain signals outside 0.15-0.4Hz to be zero;
s6-3, obtaining a frequency value corresponding to the maximum amplitude value in the frequency domain signal within the range of 0.15-0.4Hz, and obtaining the respiratory frequency.
8. A non-contact respiratory frequency monitoring system is characterized by comprising a double-light video acquisition module, a key characteristic point positioning module, a thermal infrared human face contour positioning module, a double-light registration module, a temperature change signal extraction module, a preprocessing module and a respiratory frequency calculation module;
the double-light video acquisition module is used for acquiring visible light videos and thermal infrared videos of a human face;
the key characteristic point positioning module is used for positioning key characteristic points of the human face from the visible light video;
the thermal infrared face contour positioning module is used for positioning a thermal infrared face contour from a thermal infrared video;
the double-light registration module is used for positioning a nose area in the thermal infrared face contour based on key feature points of a face in the visible light video;
the temperature change signal extraction module is used for tracking a nose area in the thermal infrared face contour in real time and acquiring a thermal infrared nose temperature change signal in a collection time period, and the temperature change signal extraction module comprises the following steps:
s4-1, acquiring a nose region coordinate P (t) of a t frame in a thermal infrared video, sampling the P (t), and training a linear regressor for calculating a response of rectangular frame sampling corresponding to a nose region through an Adaboost learning algorithm; wherein the sample is labeled with a succession of labels;
s4-2, sampling P (t) corresponding to the first 3 frames in the t +1 th frame in the thermal infrared video, and acquiring the response of each sampling by using the linear regressor;
s4-3, taking the sample with the strongest response as the nose region coordinate P (t +1) of the t +1 th frame, and calculating the formula as follows: p (t +1) ═ 1/3 (P (t-2) + P (t-1) + P (t));
s4-4, converting the thermal infrared image into a gray image, and acquiring a gray average value Signal _ lose (i) of continuous frames of a nose area in the thermal infrared video as a nose temperature change Signal of an acquisition time period; and the calculation mode of the gray average value of the nose area in the thermal infrared video in the ith frame picture is as follows:
Signal_nose(i)＝mean(Gray)；
Gray＝0.3Rnose+0.59Gnose+0.11Bnose；
wherein Gray is a Gray scale image matrix of the nose region; mean (Gray) is a function of the gray level average of the nose region in thermal infrared video, Rnose、Gnose、BnoseThree-channel image matrixes of a nose area R, G, B in the thermal infrared video respectively;
the preprocessing module is used for preprocessing the thermal infrared nose temperature change signal;
the respiratory frequency calculation module is used for acquiring respiratory frequency based on the preprocessed thermal infrared nose temperature change signal.
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