CN116386278B - Intelligent recognition reminding method, device and equipment based on infant sleeping posture - Google Patents
Intelligent recognition reminding method, device and equipment based on infant sleeping posture Download PDFInfo
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Abstract
The application relates to the technical field of image processing, solves the problem that a safety reminding message cannot be timely and accurately sent to a user when an infant is in a bad sleeping posture in the prior art, and provides an intelligent recognition reminding method, device and equipment based on the sleeping posture of the infant. The method comprises the following steps: acquiring a real-time video stream in a sleeping scene of an infant and decomposing the real-time video stream into multi-frame images; inputting the multi-frame images into a preset infant face shielding judgment model, and extracting multi-frame target images with the infant face shielded; inputting each continuous target image into a preset breath detection model to obtain infant breath state information corresponding to each continuous target image; according to the infant breathing state information, when detecting that the infant breathing state is abnormal, the infant is considered to be in a dangerous sleeping posture, and a safety prompt is sent to a user. The application reduces the suffocation probability of infants and avoids the interference of false alarm information.
Description
Technical Field
The application relates to the technical field of image processing, in particular to an intelligent recognition reminding method, device and equipment based on infant sleeping posture.
Background
Along with the development and popularization of various intelligent terminals, the application of intelligent nursing equipment is also becoming more and more widespread, and gradually becomes a part of life of people. At present, in the intelligent nursing field aiming at infants, some infants can unconsciously fall down to sleep in a sleep scene, and the lying-down sleeping posture can lead to the compression of the mouth and the nose, however, the strength of part of infants is limited, the control capability of limbs and muscles is insufficient, the sleeping posture cannot be corrected by oneself, and dangerous situations such as choking and the like are easy to occur.
The present chinese patent CN109886137a provides a method and apparatus for detecting sleeping posture of an infant, and a computer readable storage medium, the method comprising: obtaining a visible light-based video image of the infant sleeping state; processing the video image through a three-layer convolutional neural network, and judging whether a face target exists in the video image; if the face target does not exist, abnormal information is sent to the mobile terminal; and when the boundary distance between the human body target and the safety rest area is smaller than a preset value, sending out risk prompt information. According to the method, although whether the mouth and the nose of the infant are blocked or not can be identified through the deep learning algorithm of three-layer convolution, whether the infant is suffocated or not depends on whether the mouth and the nose of the infant are continuously blocked or not, and when the infant turns over and the mouth and the nose are not blocked, the scheme provided by the patent can give false alarm, so that a user is disturbed to rest.
Therefore, how to timely and accurately send out a safety reminding message to a user when the infant is in a bad sleeping position, and avoid the problem that the user is not disturbed while preventing the infant from suffocating is a urgent need to be solved.
Disclosure of Invention
In view of the above, the application provides an intelligent recognition reminding method based on the sleeping posture of an infant, which is used for solving the problem that a safety reminding message cannot be timely and accurately sent to a user when the infant is in the bad sleeping posture in the prior art.
The technical scheme adopted by the application is as follows:
in a first aspect, the application provides an intelligent recognition reminding method based on infant sleeping posture, which is characterized by comprising the following steps:
s1: acquiring a real-time video stream in a sleeping scene of an infant, and decomposing the video stream into multi-frame images;
s2: inputting the multi-frame images into a preset infant face shielding judgment model, and extracting multi-frame target images with the blocked infant faces in the multi-frame images;
s3: when a continuous multi-frame target image is detected, inputting the continuous frames of target images into a preset breath detection model to obtain infant breath state information corresponding to the continuous frames of target images;
s4: according to the infant breathing state information, when detecting that the infant breathing state is abnormal, the infant is considered to be in a dangerous sleeping posture, and a safety prompt is sent to a user.
Preferably, the S2 includes:
s21: acquiring a multi-frame image obtained by decomposing the real-time video stream;
s22: inputting the multi-frame image into a preset infant face key point detection model, and extracting an image in which the infant face key point is not detected as the target image, wherein the face key point at least comprises: nose and mouth.
Preferably, the S3 includes:
s31: determining an initial time point of each target image, and acquiring a preset duration;
s32: when the infant face shielding judgment model detects that the infant face shielding judgment model is a target image after the initial time point and outside a preset time range, determining a termination time point of the last frame of the target image;
and S33, inputting a plurality of frames of target images between the initial time point and the ending time point as continuous target images into a preset breath detection model to obtain infant breath state information corresponding to the continuous target images.
Preferably, the S33 includes:
s331: according to each continuous target image, carrying out data enhancement on image data corresponding to the respiratory motion of the infant, so as to establish a target Laplacian pyramid image representing a local area corresponding to the respiratory motion of the infant;
s332: and obtaining the infant breathing state information according to the target Laplacian pyramid image and each target image in succession.
Preferably, the S331 includes:
s3311: according to each continuous target image, establishing a Gaussian pyramid corresponding to each continuous target image, wherein the number of stages of each Gaussian pyramid is preset;
s3312: screening the corresponding continuous pixel points of each target image according to the pixel values of the pixel points in each stage of the Gaussian pyramid to obtain each target pixel point belonging to weak motion;
s3313: establishing corresponding Laplacian pyramids of all layers according to all the target pixel points;
s3314: and obtaining the target Laplacian pyramid image according to the Laplacian pyramid of each layer.
Preferably, the S332 includes:
s3321: superposing the target Laplacian pyramid image and each continuous target image to obtain a target video for carrying out data enhancement on weak motion image data;
s3322: obtaining the motion frequency of the weak motion corresponding to the target video according to the video duration and the motion times of the weak motion of the target video;
s3323: and comparing the motion frequency with a preset infant standard respiratory frequency to obtain infant respiratory state information.
Preferably, the step S4 includes:
s41: acquiring the infant breathing state information, wherein the infant breathing state information comprises: normal, shortness or no respiration;
s42: and when the infant breathing state information is rapid or no breathing, the infant breathing abnormality is considered, and a safety prompt is sent to a user.
In a second aspect, the application provides an intelligent recognition reminding device based on sleeping postures of infants, which comprises:
the image acquisition module is used for acquiring a real-time video stream in a sleeping scene of an infant and decomposing the video stream into multi-frame images;
the target image extraction module is used for inputting the multi-frame images into a preset infant face shielding judgment model and extracting multi-frame target images with the blocked infant faces in the multi-frame images;
the breath state identification module is used for inputting each continuous target image into a preset breath detection model when detecting continuous multi-frame target images to obtain infant breath state information corresponding to each continuous target image;
and the safety reminding module is used for sending safety reminding to a user according to the infant breathing state information and when detecting that the infant breathing state is abnormal, considering that the infant is in a dangerous sleeping posture.
In a third aspect, an embodiment of the present application further provides an electronic device, including: at least one processor, at least one memory and computer program instructions stored in the memory, which when executed by the processor, implement the method as in the first aspect of the embodiments described above.
In a fourth aspect, embodiments of the present application also provide a storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method as in the first aspect of the embodiments described above.
In summary, the beneficial effects of the application are as follows:
the application provides an intelligent recognition reminding method, device and equipment based on infant sleeping posture, wherein the method comprises the following steps: acquiring a real-time video stream in a sleeping scene of an infant, and decomposing the video stream into multi-frame images; inputting the multi-frame images into a preset infant face shielding judgment model, and extracting multi-frame target images with the blocked infant faces in the multi-frame images; when continuous multi-frame target images are detected, inputting each continuous target image into a preset breath detection model to obtain infant breath state information corresponding to each continuous target image; according to the infant breathing state information, when detecting that the infant breathing state is abnormal, the infant is considered to be in a dangerous sleeping posture, and a safety prompt is sent to a user. On one hand, as the occlusion of the infant face is a dynamic continuous process, the accuracy of the occlusion judgment of the infant face is improved by extracting continuous multi-frame target images to perform breath detection; on the other hand, after the face of the infant is successfully shielded and judged, the respiratory state information of the infant is obtained by further utilizing the respiratory detection model, and the mode of combining the facial shielding judgment and the respiratory detection further reduces the suffocation probability of the infant, is beneficial to the physical and mental health growth of the infant, also avoids the dangerous sleeping gesture from being mistakenly detected, prevents the user from being interfered by the false alarm information, and further improves the nursing experience of the user.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described, and it is within the scope of the present application to obtain other drawings according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart illustrating the overall operation of the intelligent recognition reminding method based on the sleeping posture of the infant in embodiment 1 of the application;
FIG. 2 is a flow chart of the shielding judgment of the infant face in embodiment 1 of the present application;
FIG. 3 is a flow chart of the detection of infant respiration in embodiment 1 of the present application;
FIG. 4 is a flow chart of determining infant breathing status information according to embodiment 1 of the present application;
fig. 5 is a schematic flow chart of extracting a target laplacian pyramid image in embodiment 1 of the present application;
FIG. 6 is a flow chart of determining respiratory status according to respiratory rate in embodiment 1 of the present application;
FIG. 7 is a flow chart of sending a security alert to a user in embodiment 1 of the present application;
fig. 8 is a block diagram of an intelligent recognition reminding device based on sleeping posture of an infant in embodiment 2 of the application;
fig. 9 is a schematic structural diagram of an electronic device in embodiment 3 of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It is noted that relational terms such as first and second, and the like are 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. In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Moreover, 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 … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element. If not conflicting, the embodiments of the present application and the features of the embodiments may be combined with each other, which are all within the protection scope of the present application.
Example 1
Referring to fig. 1, embodiment 1 of the application discloses an intelligent recognition reminding method based on sleeping postures of infants, which comprises the following steps:
s1: acquiring a real-time video stream in a sleeping scene of an infant, and decomposing the video stream into multi-frame images;
specifically, because the sleeping time of the infant is long and not fixed, the infant can be in a sleeping state in daytime and at night, the real-time video stream collected by the camera is obtained, the video stream comprises a video stream based on visible light in daytime and a video stream based on infrared light at night, the infant can be effectively cared for twenty-four hours in the sleeping state of the infant, and the obtained video stream is decomposed into multi-frame images to be input into the next program.
S2: inputting the multi-frame images into a preset infant face shielding judgment model, and extracting multi-frame target images with the blocked infant faces in the multi-frame images;
specifically, whether the face of the infant is shielded or not in the input multi-frame image is judged through a preset infant face shielding judging model, and an image with the infant face judged to be shielded is taken as a target image to be extracted, wherein the infant face is shielded, namely, the mouth and the nose of the infant are shielded, at the moment, the mouth and the nose are taken as a part of a respiratory system of a person, the infant is extremely easy to generate choking, and further analysis and processing are required to be carried out on the target image, so that the infant is prevented from generating choking.
In one embodiment, referring to fig. 2, the step S2 includes:
s21: acquiring a multi-frame image obtained by decomposing the real-time video stream;
s22: inputting the multi-frame image into a preset infant face key point detection model, and extracting an image in which the infant face key point is not detected as the target image, wherein the face key point at least comprises: nose and mouth.
Specifically, training image data of faces of infants in different angles (30 degrees, 45 degrees, 60 degrees and the like) under a large number of sleep scenes are obtained, and in an actual sleep scene, the faces of the infants are different due to different sleeping postures such as supine, lateral lying and lying prone, a large number of training image data in different angles are used as a training basis, so that the recognition accuracy of the facial feature information of the infants is effectively improved. The nose and mouth key points in the training image data are marked in advance, the marked image data are used as the basis of deep learning model training, and a target detection model based on the YoloV6s structure is constructed, and the recognition efficiency and accuracy can be improved due to the fact that YoloV6s has the characteristics of high detection accuracy and high speed. And predefining a loss index through a pre-constructed target detection model based on a YoloV6s structure, inputting the multi-frame images into the target detection model for sequential detection, wherein the larger the loss index is, the less fitting is represented between the multi-frame images and characteristic information in the target detection model, and the image with lower fitting degree with the target detection model is extracted as an image with key points of the face of the infant not detected to be used as the target image.
S3: when a continuous multi-frame target image is detected, inputting the continuous frames of target images into a preset breath detection model to obtain infant breath state information corresponding to the continuous frames of target images;
specifically, when the mouth, the nose and the like of an infant are always shielded for a period of time, the suffocation dangerous situation can occur, and in order to avoid the situation that a user is not disturbed by alarm information when the infant is not suffocated, the continuous target images are input into a preset breath detection model only when a plurality of continuous target images are detected, and infant breathing state information corresponding to the continuous target images is obtained.
In one embodiment, referring to fig. 3, the step S3 includes:
s31: determining an initial time point of each target image, and acquiring a preset duration;
specifically, for example, an initial time point corresponding to the acquisition of the extracted one frame of the target image is t 1 And obtaining a preset time length, wherein the system sets a preliminary preset time length according to the common suffocation condition of infants, and a user can also adjust the preset time length according to actual needs, wherein the preset time length is taken as an example for 3 minutes. The longer the preset duration is, the less timely the safety alarm is sent out, but the interference to the user is reduced; the shorter the preset duration is, the more timely the security alarm is issued, but the greater the possibility of interference to the user.
S32: when the infant face shielding judgment model detects that the infant face shielding judgment model is a target image after the initial time point and outside a preset time range, determining a termination time point of the last frame of the target image;
specifically, when at the initial time point t 1 When all the frame images within 3 minutes or more are target images, taking the time point when the target image of the last frame appears as a termination time point t 2 。
And S33, inputting a plurality of frames of target images between the initial time point and the ending time point as continuous target images into a preset breath detection model to obtain infant breath state information corresponding to the continuous target images.
Specifically, by subjecting said t 1 And t 2 All the target images in the range are input into a preset breath detection model, and infant breath state information corresponding to each continuous target image is obtained. Because the input images are continuous, the state of the infant can be ensured to be in a continuous face shielding state, and meanwhile, the face shielding and the breath detection are combined, so that the probability of choking of the infant is further reduced, and the healthy growth of the infant is facilitated.
In one embodiment, referring to fig. 4, the step S33 includes:
s331: according to each continuous target image, carrying out data enhancement on image data corresponding to the respiratory motion of the infant, so as to establish a target Laplacian pyramid image representing a local area corresponding to the respiratory motion of the infant;
specifically, data enhancement is performed on image data corresponding to respiratory motion in infant sleep video, specifically, the position offset distance of each pixel point of an image area caused by respiratory motion is amplified, so that Laplacian pyramid images corresponding to the respiratory motion image area are built, the Laplacian pyramid images are different in hierarchy and have different spatial frequencies and signal to noise ratios, the fewer the hierarchy is, the lower the spatial frequency is, and the built Laplacian pyramid images are preferably 3-6 layers.
In one embodiment, referring to fig. 5, the step S331 includes:
s3311: according to each continuous target image, establishing a Gaussian pyramid corresponding to each continuous target image, wherein the number of stages of each Gaussian pyramid is preset;
s3312: screening the corresponding continuous pixel points of each target image according to the pixel values of the pixel points in each stage of the Gaussian pyramid to obtain each target pixel point belonging to weak motion;
specifically, the pixel value of each pixel point in the Gaussian pyramid image is recorded as a first pixel value, and the pixel value of each pixel point in the original image is recorded as a second pixel value; and comparing each first pixel value with each second pixel value to obtain a pixel value difference value between any first pixel value and each second pixel value, comparing each pixel value difference value with a pixel value threshold, recording a second pixel value corresponding to a pixel value difference value smaller than the pixel value threshold as a target pixel value, and screening out all target pixel points by the method, wherein the target pixel points do not directly carry out Laplace change to establish a Laplace pyramid image, and the Laplace pyramid image is generated by directly carrying out Laplace change because the target pixel points can be uniformly changed, namely, the gradient of the region disappears after the Laplace change, so that the accuracy of breath detection is affected. Referring to fig. 4, fig. 4 is a diagram showing the composition of an input signal, the input signal is decomposed into wall edges, textures and smooth components, the strong edges are pixels representing the salient pixel values of the overall outline of the image, the textures are pixels with small differences of the pixel values corresponding to the pixels in the strong edges, namely, the pixels representing the details of the image area corresponding to the pixels in the strong edges, the smooth components are used for enhancing the low-frequency components and weakening the high-frequency components of the image, so as to realize the smoothing of the image, the laplace needs to conduct derivation twice, if the laplace transformation is directly conducted, the result of the processing in the color uniform change or gradual change area is 0, and the image processed in the areas becomes holes or disappears, which affects the accuracy of respiratory monitoring.
S3313: establishing corresponding Laplacian pyramids of all layers according to all the target pixel points;
specifically, after each target pixel point is obtained, a Gaussian function is utilized Adjusting each target pixel point to enable the pixel value of each pixel point in the weak motion image to show non-uniform change or non-gradual change, avoiding holes or gradient disappearance of weak motion in the established Laplacian pyramid image, referring to FIG. 5, taking-2, -1, 2 and 4 in FIG. 5 respectively to enable the pixel value of the weak motion area to be changed into non-gradual change or uniform change, avoiding zeroing the pixel value of the area after Laplacian transformation, leading to disappearance of the area image, generating a new image after completing a Gaussian pyramid image, converting the new image into a corresponding Laplacian pyramid image, and repeating the operation to finally obtain the target Laplacian pyramid image; by the method, the phenomenon that the image corresponding to the respiratory motion appears holes or disappears due to the Laplace change can be avoided, and the integrity of the respiratory motion data is ensured.
S3314: and obtaining the target Laplacian pyramid image according to the Laplacian pyramid of each layer. Specifically, obtaining Laplacian pyramids of all layers; using the formula Amplifying and superposing the Laplacian pyramid of each layer to obtain the target Laplacian pyramid image of weak motion in the infant sleeping video; wherein I (x, t) is the brightness of the pixel at time t, δ (t) is the displacement distance of the corresponding pixel at time t compared with the previous time, α is the amplification factor, and f (x) is the pixel value of the pixel x. Aiming at the generated new local Laplacian pyramid of each layer, according to the principle that the brightness of the same pixel point at the same time is unchanged, the method has the following formula:
I(x,t)＝f(x+δ(t))
then according to the principle of the constant brightness there are:
I(x,t)＝f(x+(1+α)δ(t))
considering that sleep respiratory motion belongs to low-frequency motion, low-pass filtering is performed in a low frequency band, and therefore, the first-order Taylor series expansion is as follows:
and (3) making:
b (x, t) is an image brightness variation signal corresponding to the corresponding breathing signal when the spatial point position in the video channel is x and the time is t.
The method comprises the following steps of amplifying and superposing:
then, overlapping the amplified Laplacian pyramid image with the original image to obtain pyramid images with different scales, and reconstructing the pyramid images to obtain the required amplified video; by amplifying the respiratory motion, the method is beneficial to increasing the identification speed and accuracy of the respiratory motion, thereby ensuring the accuracy of detection.
S332: and obtaining the infant breathing state information according to the target Laplacian pyramid image and each target image in succession.
In one embodiment, referring to fig. 6, the step S332 includes:
s3321: superposing the target Laplacian pyramid image and each continuous target image to obtain a target video for carrying out data enhancement on weak motion image data;
s3322: obtaining the motion frequency of the weak motion corresponding to the target video according to the video duration and the motion times of the weak motion of the target video;
s3323: and comparing the motion frequency with a preset infant standard respiratory frequency to obtain infant respiratory state information.
Specifically, weak motions comprise motions caused by respiration, hand inching of infants, waving of hair parts and the like, wave bands corresponding to the weak motions of a target video are researched to determine motion frequencies corresponding to the weak motions, and particularly, the motion frequencies are obtained according to video duration and motion times of the target video; and comparing the movement frequency with the breathing frequency of the infant to obtain the breathing state information of the infant.
S4: according to the infant breathing state information, when detecting that the infant breathing state is abnormal, the infant is considered to be in a dangerous sleeping posture, and a safety prompt is sent to a user.
In one embodiment, referring to fig. 7, the step S4 includes:
s41: acquiring the infant breathing state information, wherein the infant breathing state information comprises: normal, shortness or no respiration;
s42: and when the infant breathing state information is rapid or no breathing, the infant breathing abnormality is considered, and a safety prompt is sent to a user.
In particular, further respiratory conditions may be determined from the frequency of motion, including normal, shortness of breath, and no breath; the respiratory rate of normal adults is 16-20 times/minute and the children 30-40 times/minute. Setting the breathing frequency interval of the infant to be about 30-45 times/minute according to the data, wherein the breathing frequency interval of the infant is 40-45 times/minute; according to the respiratory rate, the sleeping video duration of each section of infant is set to be 20 seconds, namely, the wave band of 20 seconds of video is researched each time, the corresponding respiratory times in the 20 seconds of video are 10-15 times, if the times of weak motions are 10-15 times, the weak motions caused by infant respiration are considered, otherwise, the weak motions caused by other motions are considered, and in order to improve the detection accuracy, the current respiratory state can be judged by comprehensively outputting the results after the detection is carried out for a plurality of times; such as: judging according to the output results of three continuous research bands, if the corresponding frequency is not within the range of 10-15 times, considering that the frequency is not the frequency corresponding to the breathing of the infant, and immediately sending alarm information to the user; if within this range, it is considered the infant breathing rate. Through the breathing detection mode, the accuracy of dangerous sleeping posture detection is further improved, the probability of suffocation of infants is reduced, the healthy growth of the infants is facilitated, the dangerous sleeping posture is prevented from being misdetected, the user is prevented from being interfered by the misalarm information, and the nursing experience of the user is further improved.
Example 2
Referring to fig. 8, embodiment 2 of the present application further provides an intelligent recognition reminding device based on sleeping postures of infants, the device comprising:
the image acquisition module is used for acquiring a real-time video stream in a sleeping scene of an infant and decomposing the video stream into multi-frame images;
the target image extraction module is used for inputting the multi-frame images into a preset infant face shielding judgment model and extracting multi-frame target images with the blocked infant faces in the multi-frame images;
the breath state identification module is used for inputting each continuous target image into a preset breath detection model when detecting continuous multi-frame target images to obtain infant breath state information corresponding to each continuous target image;
and the safety reminding module is used for sending safety reminding to a user according to the infant breathing state information and when detecting that the infant breathing state is abnormal, considering that the infant is in a dangerous sleeping posture.
Specifically, the intelligent recognition reminding device based on the sleeping posture of the infant, which is disclosed by the embodiment 2, comprises: the image acquisition module is used for acquiring a real-time video stream in a sleeping scene of an infant and decomposing the video stream into multi-frame images; the target image extraction module is used for inputting the multi-frame images into a preset infant face shielding judgment model and extracting multi-frame target images with the blocked infant faces in the multi-frame images; the breath state identification module is used for inputting each continuous target image into a preset breath detection model when detecting continuous multi-frame target images to obtain infant breath state information corresponding to each continuous target image; and the safety reminding module is used for sending safety reminding to a user according to the infant breathing state information and when detecting that the infant breathing state is abnormal, considering that the infant is in a dangerous sleeping posture. On one hand, as the occlusion of the infant face is a dynamic continuous process, the accuracy of the occlusion judgment of the infant face is improved by extracting continuous multi-frame target images to perform breath detection; on the other hand, after the face of the infant is successfully shielded and judged, the respiratory state information of the infant is obtained by further utilizing the respiratory detection model, and the mode of combining the facial shielding judgment and the respiratory detection further reduces the suffocation probability of the infant, is beneficial to the physical and mental health growth of the infant, also avoids the dangerous sleeping gesture from being mistakenly detected, prevents the user from being interfered by the false alarm information, and further improves the nursing experience of the user.
Example 3
In addition, the intelligent recognition reminding method based on the sleeping posture of the infant according to the embodiment 1 of the application described in connection with fig. 1 can be realized by the electronic equipment. Fig. 9 shows a schematic hardware structure of an electronic device according to embodiment 3 of the present application.
The electronic device may include a processor and memory storing computer program instructions.
In particular, the processor may comprise a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
The memory may include mass storage for data or instructions. By way of example, and not limitation, the memory may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. The memory may include removable or non-removable (or fixed) media, where appropriate. The memory may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory is a non-volatile solid state memory. In a particular embodiment, the memory includes Read Only Memory (ROM). The ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these, where appropriate.
The processor reads and executes the computer program instructions stored in the memory to realize any intelligent recognition reminding method based on the sleeping posture of the infant in the embodiment.
In one example, the electronic device may also include a communication interface and a bus. The processor, the memory, and the communication interface are connected by a bus and complete communication with each other, as shown in fig. 9.
The communication interface is mainly used for realizing communication among the modules, the devices, the units and/or the equipment in the embodiment of the application.
Example 4
In addition, in combination with the intelligent recognition reminding method based on the sleeping posture of the infant in the embodiment 1, the embodiment 4 of the application can also be realized by providing a computer readable storage medium. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement any of the intelligent recognition reminding methods based on the sleeping posture of the infant in the embodiments.
In summary, the embodiment of the application provides an intelligent recognition reminding method, device and equipment based on infant sleeping posture.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (9)
1. An intelligent recognition reminding method based on infant sleeping posture is characterized by comprising the following steps:
s1: acquiring a real-time video stream in a sleeping scene of an infant, and decomposing the video stream into multi-frame images;
s2: inputting the multi-frame images into a preset infant face shielding judgment model, and extracting multi-frame target images with the blocked infant faces in the multi-frame images;
s3: when a continuous multi-frame target image is detected, inputting the continuous frames of target images into a preset breath detection model to obtain infant breath state information corresponding to the continuous frames of target images;
s4: according to the infant breathing state information, when detecting that the infant breathing state is abnormal, considering that the infant is in a dangerous sleeping posture, and sending a safety prompt to a user;
wherein, the S2 includes:
s21: acquiring a multi-frame image obtained by decomposing the real-time video stream;
s22: inputting the multi-frame image into a preset infant face key point detection model, and extracting an image in which the infant face key point is not detected as the target image, wherein the face key point at least comprises: nose and mouth.
2. The intelligent recognition reminding method based on the sleeping posture of the infant according to claim 1, wherein the step S3 comprises:
s31: determining an initial time point of each target image, and acquiring a preset duration;
s32: when the infant face shielding judgment model detects that the infant face shielding judgment model is a target image after the initial time point and outside a preset time range, determining a termination time point of the last frame of the target image;
and S33, inputting a plurality of frames of target images between the initial time point and the ending time point as continuous target images into a preset breath detection model to obtain infant breath state information corresponding to the continuous target images.
3. The intelligent recognition reminding method based on the sleeping posture of the infant according to claim 2, wherein S33 comprises:
s331: according to the continuous target images of each frame, carrying out data enhancement on image data corresponding to the breathing motion of the infant, so as to establish a target Laplacian pyramid image representing a local area corresponding to the breathing motion of the infant;
s332: and obtaining the infant breathing state information according to the target Laplacian pyramid image and the target image of each continuous frame.
4. The intelligent recognition reminding method based on the sleeping posture of the infant according to claim 3, wherein the step S331 comprises:
s3311: establishing Gaussian pyramids corresponding to the target images of the continuous frames according to the target images of the continuous frames, wherein the level of each Gaussian pyramid is preset;
s3312: screening the corresponding continuous pixel points of the target image of each frame according to the pixel values of the pixel points in each stage of the Gaussian pyramid to obtain each target pixel point belonging to weak motion;
s3313: establishing corresponding Laplacian pyramids of all layers according to all the target pixel points;
s3314: and obtaining the target Laplacian pyramid image according to the Laplacian pyramid of each layer.
5. The intelligent recognition reminding method based on the sleeping posture of the infant according to claim 4, wherein S332 comprises:
s3321: superposing the target Laplacian pyramid image and the target image of each continuous frame to obtain a target video for carrying out data enhancement on weak motion image data;
s3322: obtaining the motion frequency of the weak motion corresponding to the target video according to the video duration and the motion times of the weak motion of the target video;
s3323: and comparing the motion frequency with a preset infant standard respiratory frequency to obtain infant respiratory state information.
6. The intelligent recognition reminding method based on the sleeping posture of the infant according to claim 1, wherein the step S4 comprises:
s41: acquiring the infant breathing state information, wherein the infant breathing state information comprises: normal, shortness or no respiration;
s42: and when the infant breathing state information is rapid or no breathing, the infant breathing abnormality is considered, and a safety prompt is sent to a user.
7. Intelligent recognition reminding device based on infant sleeping gesture, its characterized in that, the device includes:
the image acquisition module is used for acquiring a real-time video stream in a sleeping scene of an infant and decomposing the video stream into multi-frame images;
the target image extraction module is used for inputting the multi-frame images into a preset infant face shielding judgment model and extracting multi-frame target images with the blocked infant faces in the multi-frame images;
the breath state identification module is used for inputting each continuous target image into a preset breath detection model when detecting continuous multi-frame target images to obtain infant breath state information corresponding to each continuous target image;
the safety reminding module is used for considering that the infant is in a dangerous sleeping posture when the abnormal infant breathing state is detected according to the infant breathing state information, and sending a safety reminder to a user;
inputting the multi-frame image into a preset infant face shielding judgment model, and extracting the multi-frame target image with the blocked infant face in the multi-frame image comprises the following steps:
acquiring a multi-frame image obtained by decomposing the real-time video stream;
inputting the multi-frame image into a preset infant face key point detection model, and extracting an image in which the infant face key point is not detected as the target image, wherein the face key point at least comprises: nose and mouth.
8. An electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of any one of claims 1-6.
9. A storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1-6.
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