WO2021017307A1

WO2021017307A1 - Non-contact heart rate measurement method, system, device, and storage medium

Info

Publication number: WO2021017307A1
Application number: PCT/CN2019/118082
Authority: WO
Inventors: 王义文; 郑权; 王健宗
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-07-31
Filing date: 2019-11-13
Publication date: 2021-02-04
Also published as: CN110547783B; CN110547783A

Abstract

A non-contact heart rate measurement system, device, storage medium, and method. The method comprises: acquiring a plurality of frames of face images in a video to be processed (S100); extracting a preset facial region in the plurality of frames of face images (S102); acquiring pixel value matrices of an R channel, a G channel, and a B channel of the preset facial region, and selecting one of the channels as a target channel (S104); enlarging, through Euler image enlargement, the pixel value matrix corresponding to the target channel, to obtain a cardiovascular pulse wave sequence (S106); analyzing the frequency waveform of the cardiovascular pulse wave sequence, and selecting, as a target frequency, a frequency value corresponding to a largest peak of the peaks of the frequency waveform (S108); and calculating a heart rate value according to the target frequency (S110). This method can control an absolute error between the calculated heart rate value and an actual heart rate to be within 3, improving the accuracy of measurement.

Description

Non-contact heart rate detection method, system, equipment and storage medium

This application requires that it be submitted to the Chinese Patent Office on July 31, 2019. The application number and the patent name are respectively 201910699269.6, and the priority of the Chinese patent application for the invention patent of "non-contact heart rate detection method, system, equipment and storage medium", which The entire content is incorporated into this application by reference.

Technical field

The embodiments of the present application relate to the field of heart rate measurement, and in particular, to a non-contact heart rate detection method, system, device, and storage medium.

technical background

Heart rate refers to the number of heartbeats per minute, which is one of the important parameters of human metabolism and functional activities. For example, the increase in resting heart rate is widely regarded as an independent risk factor for the detection of cardiovascular disease. Routine testing of resting heart rate is beneficial to the prevention and rehabilitation of cardiovascular diseases.

At present, in the current biomedical research field, many researchers have proposed different non-contact measurement of vital signs, such as heart rate (HR, heart rate) and respiratory rate (RR, respiratorn rate) solutions, including laser Doppler, microwave Doppler radar and thermal imaging methods. The non-contact evaluation of HR variability (HRV, Heart rate variability) is an indicator of cardiac autonomic nerve activity, and it is a greater challenge. However, the inventor found that some studies have tried to evaluate HRV in the past and have made impressive progress, but there is a common disadvantage that the system is expensive and requires specialized hardware, and the electronic hardware has a life span, which will affect the accuracy of the test.

Summary of the invention

In view of this, the purpose of the embodiments of the present application is to provide a non-contact heart rate detection method, system, device, and storage medium, which can make the calculation of the heart rate value and the absolute value of the true heart rate error within 3, thereby improving the accuracy of detection .

In order to achieve the foregoing objective, an embodiment of the present application provides a non-contact heart rate detection method, including:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

Acquiring the pixel value matrix of the R channel, the G channel and the B channel of the preset face region, and selecting one of the R channel, the G channel and the B channel as the target channel;

Enlarging the pixel value matrix corresponding to the target channel through Euler image enlargement to obtain a cardiovascular pulse wave sequence;

Analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency;

The heart rate value is calculated according to the target frequency.

In order to achieve the foregoing objective, an embodiment of the present application also provides a non-contact heart rate detection system, including:

An acquisition module for acquiring multiple frames of face images in the video to be processed;

An extraction module for extracting preset facial regions in the multi-frame face image;

The selection module is used to obtain the pixel value matrix of the R channel, the G channel and the B channel of the preset face area, and select one of the R channel, the G channel and the B channel as the target channel;

An amplifying module, configured to amplify the pixel value matrix corresponding to the target channel through Euler image magnification to obtain a cardiovascular pulse wave sequence;

The analysis module is used to analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency;

The calculation module is used to calculate the heart rate value according to the target frequency.

In order to achieve the foregoing objective, an embodiment of the present application further provides a computer device, the computer device includes a memory and a processor, the memory stores computer-readable instructions that can run on the processor, and the computer When the readable instructions are executed by the processor, the following steps are implemented:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

The heart rate value is calculated according to the target frequency.

In order to achieve the above objective, the embodiments of the present application also provide a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions may Is executed by at least one processor, so that the at least one processor executes the following steps:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

The heart rate value is calculated according to the target frequency.

The non-contact heart rate detection method, system, device and storage medium provided by the embodiments of the application select the face part from the video, and use Euler image magnification algorithm to magnify the pixel value matrix to obtain the blood vessel pulse wave sequence. The waveform of the vascular pulse wave sequence is analyzed, and the heart rate value is finally calculated; the embodiment of the present application can make the calculation of the heart rate and the absolute value of the true heart rate have an error within 3, which improves the accuracy of detection.

Description of the drawings

FIG. 1 is a flowchart of Embodiment 1 of a non-contact heart rate detection method according to an embodiment of this application.

Fig. 2 is a flowchart of step S100 in Fig. 1 of an embodiment of the application.

Fig. 3 is a flowchart of step S104 in Fig. 1 of the embodiment of the application.

Fig. 4 is a flowchart of step S104A3 in Fig. 1 of the embodiment of the application.

Fig. 5 is a flowchart of step S106 in Fig. 1 of the embodiment of the application.

Fig. 6 is a flowchart of step S106B in Fig. 1 of the embodiment of the application.

FIG. 7 is a schematic diagram of program modules of Embodiment 2 of the non-contact heart rate detection system according to the embodiment of the application.

FIG. 8 is a schematic diagram of the hardware structure of the third embodiment of the computer equipment of this application.

Detailed ways

In order to make the purpose, technical solutions, and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and not used to limit the application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Example one

Referring to Fig. 1, there is shown a flow chart of the non-contact heart rate detection method in the first embodiment of the present application. It can be understood that the flowchart in this method embodiment is not used to limit the order of execution of the steps. The following exemplarily describes the computer device 2 as the execution subject. details as follows.

Step S100: Obtain multiple frames of face images in the video to be processed.

Exemplarily, referring to FIG. 2, step S100 includes:

Step S100A, acquiring the face image information of each frame of image in the video information frame by frame according to time sequence;

Step S100B: Count the number of image frames of valid images, where the valid images are one or more images containing face image information in the video information;

In step S100C, when the number of image frames of the effective image is greater than a preset threshold, proceed to the next step to stop acquiring the face image information of each frame of the image in the video information.

Specifically, when the number of image frames of the effective image is greater than the preset threshold, it is preliminarily determined that there is a living body, otherwise it is determined that there is no living body. This step is used to determine whether the user to be tested has entered the shooting range.

Step S102: Extract a preset face region in the multi-frame face image.

Specifically, in order to avoid noise interference caused by the use of the entire face, the nose area and forehead area of the face image information can be selected as the preset face area. The nose area and forehead area are richer in capillaries, thus having better heart rate detection. Effective and low noise interference. The nose triangle area includes three feature points, and the forehead area includes four feature points. The feature points of each area form a topological structure. These topological structures represent the graph model of these areas. Each feature point is the node of these topological structures. These nodes It is mainly extracted and filtered through scale-invariant feature transformation matching algorithm and node position relationship; then, refined feature extraction is performed on each node neighborhood, and the distance between nodes is calibrated, which forms the nose triangle area and forehead The feature point map of the four key areas.

Step S104: Obtain the pixel value matrix of the R channel, the G channel, and the B channel of the preset face area, and select one of the R channel, the G channel and the B channel as the target channel.

Specifically, the area pixel value matrix of each pixel in the preset face area can be obtained through an image algorithm (such as the OpenCV algorithm). The preset face area can be 50mm*50mm, and the extracted pixel value matrix can be a 50*50*3 area pixel value matrix. Each data in the area pixel value matrix is used to represent the corresponding pixel in a certain channel of each frame The pixel value of the point.

Exemplarily, referring to FIG. 3, step S104 includes:

In step S104A, the pixel value matrix of each channel of the R channel, the G channel, and the B channel is transformed in the frequency domain through a fast Fourier transform to obtain the channel energy of each channel.

Further, referring to FIG. 4, step S104A further includes:

Step S104A1, according to obtaining the area pixel value matrix of each of the R channel, G channel, and B channel in each frame;

Step S104A2, splicing the regional pixel value matrices of each of the R channel, G channel and B channel frame by frame to obtain a pixel value matrix;

Step S104A3, calculate the channel energy of each channel.

Specifically, the channel energy represents the change value of the pixel value matrix of each R channel, G channel, and B channel.

Exemplarily, step S104A3 includes:

The pixel value matrix of each of the R channel, the G channel, and the B channel is transformed into the frequency domain through a fast Fourier transform:

The pixel value matrix of each channel is represented by x(n), and x(n) is decomposed into the sum of two sequences of even and odd numbers, namely:

x(n)=x ₁ (n)+x ₂ (n);

The time length of x ₁ (n) and x ₂ (n) are both N/2, N represents the time length of each channel selection, x ₁ (n) is an even sequence, x ₂ (n) is an odd sequence, and then pixel The value matrix performs fast Fourier transform operation, and the fast Fourier transform calculation formula is as follows:

The value of X(k) of each channel is calculated to obtain the channel energy.

In step S104B, the channel with the largest energy is selected as the target channel.

Specifically, the channel corresponding to the maximum X(k) value is taken as the target channel, which indicates that the change value of the target channel is the largest.

Step S106: Enlarge the pixel value matrix corresponding to the target channel through Euler image enlargement to obtain a cardiovascular pulse wave sequence.

Specifically, the embodiment of the present application amplifies the energy channel of the pixel value matrix of the target channel, and the signal-to-noise ratios of different basebands should be relatively close. Therefore, a Gaussian pyramid can be selected to down-sampling and low-pass filtering the target channel.

Exemplarily, referring to FIG. 5, step S106 includes:

Step S106A, spatially filtering the pixel value matrix corresponding to the target channel to obtain basebands of different spatial frequencies, wherein a low-pass filter is used for spatial filtering;

In step S106B, the baseband is smoothed and down-sampled according to the Gaussian pyramid to obtain the cardiovascular pulse wave sequence.

Exemplarily, referring to FIG. 6, step S106B includes:

Step S106B1, the first-level Gaussian pyramid obtains the second-level Gaussian image through smoothing and down-sampling, and the cut-off frequency of the Gaussian pyramid gradually increases by a factor of 2 from the upper level to the next level;

Step S106B2, until the K-1 Gaussian pyramid is smoothed and down-sampling to obtain the K-th Gaussian image, and the cardiovascular pulse wave time sequence is obtained.

Specifically, if it is necessary to reconstruct the pixel value matrix of the target channel after the Euler image is enlarged. During reconstruction, only the smallest level of the Gaussian pyramid needs to be up-sampled (because the pixel value matrix of the target channel is regarded as the smallest level of the baseband for down-sampling when the Euler image is enlarged), and finally the R of the video to be processed The pixel value matrix of each channel in the channel, G channel and B channel can be superimposed to obtain the pixel value matrix of the target channel after Euler image enlargement, which can more obviously display the color change in the video under test.

The use of Euler image amplification technology helps to reduce noise, and the image shows different signal-to-noise ratios at different spatial frequencies. Generally speaking, the lower the spatial frequency, the higher the signal-to-noise ratio. Therefore, to prevent distortion, these basebands should use different magnifications. For the topmost image, that is, the image with the lowest spatial frequency and the highest signal-to-noise ratio, the maximum magnification can be used, and the magnification of the next layer decreases in turn. Facilitate the approximation of the image signal.

Step S108: Analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency.

Specifically, the frequency waveform of the cardiovascular pulse wave sequence is band-pass filtered, and the frequency range of the human heart rate is selected for band-pass filtering. The band-pass filter can be a Butterworth band-pass filter or an ideal band-pass filter. Remove noise and other frequency domain interference to heart rate prediction. Different bandpass filters can be selected according to different needs. If you need to perform subsequent time-frequency analysis of the cardiovascular pulse wave sequence, you can choose the ideal bandpass filter; if you do not need to perform the time-frequency analysis of the cardiovascular pulse wave sequence, you can choose a filter with a wide passband, such as Butterworth Bandpass filter, secondary IIR filter, etc. The ideal low-pass filter is selected for this application.

Exemplarily, step S108 includes:

The frequency bandwidth 0.4-4Hz (24-240bpm) is selected as the analysis frequency band, and the frequency waveform of the cardiovascular pulse wave sequence is band-pass filtered to obtain the peaks of the frequency waveforms of the cardiovascular pulse wave sequence, wherein the maximum peak value corresponds to The frequency is the target frequency.

Step S110, calculating a heart rate value according to the target frequency.

Specifically, when the face color difference of the adjacent multiple frames of face images is greater than the preset threshold (that is, the face of the user to be tested is basically in a static state), the change of the face color comes from the blood change caused by the heartbeat, which can be measured from the heartbeat. It can be seen from the waveform of the frequency waveform of the vascular pulse wave sequence that the heart rate, the number of heartbeats per minute, is equal to 60 times the target frequency.

Example two

Please continue to refer to FIG. 7, which shows a schematic diagram of the program modules of the second embodiment of the non-contact heart rate detection system of the present application. In this embodiment, the non-contact heart rate detection system 20 may include or be divided into one or more program modules. The one or more program modules are stored in a storage medium and executed by one or more processors. In order to complete this application, the above non-contact heart rate detection method can be realized. The program module referred to in the embodiments of the present application refers to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment:

The acquiring module 200 is used to acquire multiple frames of face images in the video to be processed.

Exemplarily, the obtaining module 200 is further used for:

Acquiring the face image information of each frame of the image in the video information frame by frame according to time sequence;

Counting the number of image frames of valid images, where the valid images are one or more images containing face image information in the video information;

When the number of image frames of the valid image is greater than the preset threshold, the next step is to stop acquiring the face image information of each frame of the video information.

The extraction module 202 is configured to extract preset facial regions in the multi-frame face image.

Specifically, in order to avoid noise interference caused by the use of the entire face, the nose area and forehead area of the face image information can be selected as the preset face area. The nose area and forehead area are richer in capillaries, thus having better heart rate detection. Effective and low noise interference.

The selecting module 204 is configured to obtain the pixel value matrix of the R channel, the G channel, and the B channel of the preset face region, and select one of the R channel, the G channel, and the B channel as the target channel.

Exemplarily, the selection module 204 is also used for:

The pixel value matrix of each channel of the R channel, the G channel, and the B channel is transformed into the frequency domain through a fast Fourier transform to obtain the channel energy of each channel.

Further, according to obtaining the regional pixel value matrix of each of the R channel, G channel, and B channel in each frame;

Splicing the regional pixel value matrices of each of the R channel, G channel, and B channel frame by frame to obtain a pixel value matrix;

Calculate the channel energy of each channel.

Exemplarily, the pixel value matrix of each of the R channel, the G channel, and the B channel is transformed into the frequency domain through a fast Fourier transform:

x(n)=x ₁ (n)+x ₂ (n);

The time length of x ₁ (n) and x ₂ (n) are both N/2, N represents the time length of each channel selection, x ₁ (n) is an even sequence, x ₂ (n) is an odd sequence, and then the pixel The value matrix performs fast Fourier transform operation, and the fast Fourier transform calculation formula is as follows:

The value of X(k) of each channel is calculated to obtain the channel energy.

The channel with the largest energy is selected as the target channel, that is, the channel corresponding to the maximum X(k) value is selected as the target channel.

The magnification module 206 is used to magnify the pixel value matrix corresponding to the target channel through Euler image magnification to obtain a cardiovascular pulse wave sequence.

Exemplarily, the amplification module 206 is also used for:

Spatially filtering the pixel value matrix corresponding to the target channel to obtain basebands of different spatial frequencies, wherein a low-pass filter is used for spatial filtering;

The baseband is smoothed and down-sampled according to the Gaussian pyramid to obtain the cardiovascular pulse wave sequence.

Exemplarily, the first-level Gaussian pyramid obtains the second-level Gaussian image through smoothing and down-sampling, and the cut-off frequency of the Gaussian pyramid gradually increases by a factor of 2 from the upper level to the next level;

Until the K-1 level Gaussian pyramid is smoothed and down-sampling, the K level Gaussian image is obtained, and the cardiovascular pulse wave time sequence is obtained.

The analysis module 208 is configured to analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency.

Exemplarily, the analysis module 208 is further used for:

The calculation module 210 is configured to calculate a heart rate value according to the target frequency.

Example three

Refer to FIG. 8, which is a schematic diagram of the hardware architecture of the computer device according to the third embodiment of the present application. In this embodiment, the computer device 2 is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions. The computer device 2 may be a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or a server cluster composed of multiple servers). As shown in FIG. 8, the computer device 2 at least includes, but is not limited to, a memory 21, a processor 22, a network interface 23, and a non-contact heart rate detection system 20 that can communicate with each other through a system bus. among them:

In this embodiment, the memory 21 includes at least one type of non-volatile computer-readable storage medium. The readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), Random access memory (RAM), static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk Wait. In some embodiments, the memory 21 may be an internal storage unit of the computer device 2, such as a hard disk or memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, for example, a plug-in hard disk, a smart media card (SMC), and a secure digital (Secure Digital, SD card, Flash Card, etc. Of course, the memory 21 may also include both the internal storage unit of the computer device 2 and its external storage device. In this embodiment, the memory 21 is generally used to store the operating system and various application software installed in the computer device 2, for example, the program code of the non-contact heart rate detection system 20 in the second embodiment. In addition, the memory 21 can also be used to temporarily store various types of data that have been output or will be output.

The processor 22 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 22 is generally used to control the overall operation of the computer device 2. In this embodiment, the processor 22 is used to run the program code or process data stored in the memory 21, for example, to run the non-contact heart rate detection system 20 to implement the non-contact heart rate detection method of the first embodiment.

The network interface 23 may include a wireless network interface or a wired network interface. The network interface 23 is generally used to establish a communication connection between the server 2 and other electronic devices. For example, the network interface 23 is used to connect the server 2 to an external terminal through a network, and to establish a data transmission channel and a communication connection between the server 2 and the external terminal. The network may be Intranet, Internet, Global System of Mobile Communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G Network, Bluetooth (Bluetooth), Wi-Fi and other wireless or wired networks. It should be pointed out that FIG. 8 only shows the computer device 2 with components 20-23, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

In this embodiment, the non-contact heart rate detection system 20 stored in the memory 21 may also be divided into one or more program modules, and the one or more program modules are stored in the memory 21 and configured by One or more processors (the processor 22 in this embodiment) are executed to complete the application.

For example, FIG. 7 shows a schematic diagram of the program modules of the second embodiment of the non-contact heart rate detection system 20. In this embodiment, the non-contact heart rate detection system 20 can be divided into an acquisition module 200 and an extraction module 202. , The selection module 204, the amplification module 206, the analysis module 208, and the calculation module 210. Among them, the program module referred to in this application refers to a series of computer program instruction segments that can complete specific functions. The specific functions of the program modules 200-210 have been described in detail in the second embodiment, and will not be repeated here.

Example four

This embodiment also provides a non-volatile computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory ( SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, server, App application mall, etc., on which storage There are computer-readable instructions, and the corresponding functions are realized when the program is executed by the processor. The non-volatile computer-readable storage medium of this embodiment is used to store the non-contact heart rate detection system 20, and when executed by the processor, the following steps are implemented:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

The heart rate value is calculated according to the target frequency.

In one embodiment, when the computer-readable instructions are executed by the processor, the following steps are further implemented:

Performing frequency domain transformation on the pixel value matrix of each of the R channel, G channel, and B channel through a fast Fourier transform to obtain the channel energy of each channel;

The channel with the largest energy is selected as the target channel.

The serial numbers of the foregoing embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。

The above are only preferred embodiments of this application, and do not limit the scope of this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of this application, or directly or indirectly used in other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims

A non-contact heart rate detection method, including:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

Acquiring the pixel value matrix of the R channel, the G channel and the B channel of the preset face region, and selecting one of the R channel, the G channel and the B channel as the target channel;

Enlarging the pixel value matrix corresponding to the target channel through Euler image enlargement to obtain a cardiovascular pulse wave sequence;

Analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency;

The heart rate value is calculated according to the target frequency.
The non-contact heart rate detection method according to claim 1, obtaining the pixel value matrix of the R channel, the G channel and the B channel of the preset face area, and select one of them from the R channel, G channel and B channel The steps for a channel as a target channel include:

Performing frequency domain transformation on the pixel value matrix of each of the R channel, G channel, and B channel through a fast Fourier transform to obtain the channel energy of each channel;

The channel with the largest energy is selected as the target channel.
The non-contact heart rate detection method according to claim 1, wherein the step of amplifying the pixel value matrix corresponding to the target channel through Euler image magnification to obtain a cardiovascular pulse wave sequence comprises:

Spatially filtering the pixel value matrix corresponding to the target channel to obtain basebands of different spatial frequencies, wherein a low-pass filter is used for spatial filtering;

The baseband is smoothed and down-sampled according to the Gaussian pyramid to obtain the cardiovascular pulse wave sequence.
The non-contact heart rate detection method according to claim 3, wherein the step of smoothing and down-sampling the baseband according to a Gaussian pyramid to obtain the cardiovascular pulse wave sequence comprises:

The first-level Gaussian pyramid obtains the second-level Gaussian image through smoothing and down-sampling. The cut-off frequency of the Gaussian pyramid gradually increases by a factor of 2 from the upper layer to the next layer;

Until the K-1 level Gaussian pyramid is smoothed and down-sampling, the K level Gaussian image is obtained, and the cardiovascular pulse wave time sequence is obtained.
The non-contact heart rate detection method according to claim 1, analyzing the frequency waveform of the cardiovascular pulse wave sequence, and selecting the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency, comprising :

A frequency bandwidth of 0.4-4 Hz is selected as the analysis frequency band, and the frequency waveform of the cardiovascular pulse wave sequence is band-pass filtered to obtain the peak of the frequency waveform of the cardiovascular pulse wave sequence, where the frequency corresponding to the maximum peak peak value is the target frequency.
According to the non-contact heart rate detection method according to claim 1, the step of obtaining multiple frames of face image information in the video information to be processed includes:

Acquiring the face image information of each frame of the image in the video information frame by frame according to time sequence;

Counting the number of image frames of valid images, where the valid images are one or more images containing face image information in the video information;

When the number of image frames of the effective image is greater than the preset threshold, stop acquiring the face image information of each frame of the image in the video information.
A non-contact heart rate detection system, including:

An acquisition module for acquiring multiple frames of face images in the video to be processed;

An extraction module for extracting preset facial regions in the multi-frame face image;

The selection module is used to obtain the pixel value matrix of the R channel, the G channel and the B channel of the preset face area, and select one of the R channel, the G channel and the B channel as the target channel;

An amplifying module, configured to amplify the pixel value matrix corresponding to the target channel through Euler image magnification to obtain a cardiovascular pulse wave sequence;

The analysis module is used to analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency;

The calculation module is used to calculate the heart rate value according to the target frequency.
The non-contact heart rate detection system according to claim 7, wherein the acquisition module is further used for:

Acquiring the face image information of each frame of the image in the video information frame by frame according to time sequence;

Counting the number of image frames of valid images, where the valid images are one or more images containing face image information in the video information;

When the number of image frames of the effective image is greater than the preset threshold, stop acquiring the face image information of each frame of the image in the video information.
The non-contact heart rate detection system according to claim 7, wherein the selection module is further used for:

Performing frequency domain transformation on the pixel value matrix of each of the R channel, G channel, and B channel through a fast Fourier transform to obtain the channel energy of each channel;

The channel with the largest energy is selected as the target channel.
The non-contact heart rate detection system according to claim 7, wherein the amplification module is further used for:

Spatially filtering the pixel value matrix corresponding to the target channel to obtain basebands of different spatial frequencies, wherein a low-pass filter is used for spatial filtering;

The baseband is smoothed and down-sampled according to the Gaussian pyramid to obtain the cardiovascular pulse wave sequence.
The non-contact heart rate detection system according to claim 10, the amplification module is further used for:

The first-level Gaussian pyramid obtains the second-level Gaussian image through smoothing and down-sampling. The cut-off frequency of the Gaussian pyramid gradually increases by a factor of 2 from the upper layer to the next layer;

Until the K-1 level Gaussian pyramid is smoothed and down-sampling, the K level Gaussian image is obtained, and the cardiovascular pulse wave time sequence is obtained.
The non-contact heart rate detection system according to claim 7, wherein the analysis module is further used for:

A frequency bandwidth of 0.4-4 Hz is selected as the analysis frequency band, and the frequency waveform of the cardiovascular pulse wave sequence is band-pass filtered to obtain the peak of the frequency waveform of the cardiovascular pulse wave sequence, where the frequency corresponding to the maximum peak peak value is the target frequency.
A computer device, the computer device includes a memory and a processor, the memory stores computer readable instructions that can run on the processor, and the computer readable instructions when executed by the processor realize the following step:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

Acquiring the pixel value matrix of the R channel, the G channel and the B channel of the preset face region, and selecting one of the R channel, the G channel and the B channel as the target channel;

Enlarging the pixel value matrix corresponding to the target channel through Euler image enlargement to obtain a cardiovascular pulse wave sequence;

Analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency;

The heart rate value is calculated according to the target frequency.
The computer device according to claim 13, wherein the computer-readable instructions, when executed by the processor, implement the following steps:

Acquiring the face image information of each frame of the image in the video information frame by frame according to time sequence;

Counting the number of image frames of valid images, where the valid images are one or more images containing face image information in the video information;

When the number of image frames of the effective image is greater than the preset threshold, stop acquiring the face image information of each frame of the image in the video information.
The computer device according to claim 13, wherein the computer-readable instructions, when executed by the processor, implement the following steps:

Performing frequency domain transformation on the pixel value matrix of each of the R channel, G channel, and B channel through a fast Fourier transform to obtain the channel energy of each channel;

The channel with the largest energy is selected as the target channel.
The computer device according to claim 13, wherein the computer-readable instructions, when executed by the processor, implement the following steps:

Spatially filtering the pixel value matrix corresponding to the target channel to obtain basebands of different spatial frequencies, wherein a low-pass filter is used for spatial filtering;

The baseband is smoothed and down-sampled according to the Gaussian pyramid to obtain the cardiovascular pulse wave sequence.
The computer device according to claim 16, wherein the computer-readable instructions, when executed by the processor, implement the following steps:

The first-level Gaussian pyramid obtains the second-level Gaussian image through smoothing and down-sampling. The cut-off frequency of the Gaussian pyramid gradually increases by a factor of 2 from the upper layer to the next layer;

Until the K-1 level Gaussian pyramid is smoothed and down-sampling, the K level Gaussian image is obtained, and the cardiovascular pulse wave time sequence is obtained.
The computer device according to claim 13, wherein the computer-readable instructions, when executed by the processor, implement the following steps:

A frequency bandwidth of 0.4-4 Hz is selected as the analysis frequency band, and the frequency waveform of the cardiovascular pulse wave sequence is band-pass filtered to obtain the peak of the frequency waveform of the cardiovascular pulse wave sequence, where the frequency corresponding to the maximum peak peak value is the target frequency.
A non-volatile computer-readable storage medium in which computer-readable instructions are stored, and the computer-readable instructions can be executed by at least one processor to cause the At least one processor performs the following steps:

Obtain multiple frames of face images in the video to be processed;

Extracting preset facial regions in the multi-frame face images;

Acquiring the pixel value matrix of the R channel, the G channel and the B channel of the preset face region, and selecting one of the R channel, the G channel and the B channel as the target channel;

Enlarging the pixel value matrix corresponding to the target channel through Euler image enlargement to obtain a cardiovascular pulse wave sequence;

Analyze the frequency waveform of the cardiovascular pulse wave sequence, and select the frequency value corresponding to the largest peak among the peaks of the frequency waveform as the target frequency;

The heart rate value is calculated according to the target frequency.
According to the non-volatile computer-readable storage medium of claim 19, when the computer-readable instructions are executed by the processor, the following steps are further implemented:

Performing frequency domain transformation on the pixel value matrix of each of the R channel, the G channel, and the B channel through a fast Fourier transform to obtain the channel energy of each channel;

The channel with the largest energy is selected as the target channel.