CN115690893A - Information detection method, information detection device, information detection medium, and electronic device - Google Patents
Information detection method, information detection device, information detection medium, and electronic device Download PDFInfo
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Abstract
The disclosure relates to an information detection method, an information detection device, an information detection medium and an electronic device. The method comprises the following steps: acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located; determining the gesture category of the user according to the upper body image and the desktop image; and if the posture category is the bad sitting posture, generating a first prompt message. Like this, the user can in time correct the position of sitting through first suggestion message, has reduced because bad position of sitting leads to near-sighted risk and probability to can avoid the user because of the waist back damage, the muscle strain scheduling problem that bad position of sitting leads to. In addition, when the gesture category of the user is determined, the upper body image of the user and the desktop image are considered simultaneously, so that the detection precision of the gesture category is improved, and the trouble of the user caused by the fact that the first prompt message is generated by mistake is avoided.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to an information detection method, apparatus, medium, and electronic device.
Background
In the process that a user needs to use eyes during ordinary study and work, the problems of myopia, waist and back injuries, muscle strain and the like are easily caused by poor sitting posture. Although the user can pay attention after being reminded by others, the user cannot remind the user at any moment. From this, some devices that remind the user to correct bad position of sitting have appeared, and these devices are real-time detection user's position of sitting usually to when detecting the user position of sitting for bad position of sitting, remind the user in time to correct the position of sitting, but the degree of accuracy that the position of sitting detected is difficult to guarantee, thereby the mistake is reminded probably appears, causes the puzzlement for the user.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides an information detection method, applied to a first terminal, including:
acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located;
determining the gesture category of the user according to the upper body image and the desktop image;
and if the posture category is the bad sitting posture, generating a first prompt message.
In a second aspect, the present disclosure provides an information detecting apparatus, applied to a first terminal, including:
the acquisition module is used for acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located;
the determining module is used for determining the gesture category of the user according to the upper body image and the desktop image acquired by the acquiring module;
and the prompting module is used for generating a first prompting message if the posture category determined by the determining module is an undesirable sitting posture.
In a third aspect, the present disclosure provides a computer readable medium having stored thereon a computer program which, when executed by a processing apparatus, performs the steps of the method provided by the first aspect of the present disclosure.
In a fourth aspect, the present disclosure provides an electronic device comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to implement the steps of the method provided by the first aspect of the present disclosure.
In the technical scheme, after the upper half body image of the user and the desktop image of the desktop where the first terminal is located are obtained, the posture category of the user is determined according to the upper half body image of the user and the desktop image; and if the posture category is the poor sitting posture, generating a first prompt message. Like this, the user can in time correct the position of sitting through first suggestion message, has reduced because bad position of sitting leads to near-sighted risk and probability to can avoid the user because of the waist back damage, the muscle strain scheduling problem that bad position of sitting leads to. In addition, when the gesture category of the user is determined, the upper body image of the user and the desktop image are considered simultaneously, so that the detection precision of the gesture category is improved, and the trouble of the user caused by mistakenly generating the first prompt message is avoided.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale. In the drawings:
fig. 1 is a flow chart illustrating a method of information detection according to an example embodiment.
FIG. 2 is a schematic diagram illustrating a configuration of a gesture detection model according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating an information detection method according to another exemplary embodiment.
Fig. 4 is a flow chart illustrating a method of information detection according to another exemplary embodiment.
FIG. 5 is a block diagram illustrating an information detection apparatus according to an example embodiment.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Fig. 1 is a flowchart illustrating an information detection method according to an exemplary embodiment, where the information detection method may be applied to a first terminal, and the first terminal may be, for example, a desk lamp, a smart terminal (e.g., a smart phone, a tablet computer, etc.) placed on a desktop through a stand. As shown in fig. 1, the information detection method may include S101 to S103.
In S101, an upper body image of the user and a desktop image of a desktop on which the first terminal is located are obtained.
In the disclosure, the upper half body image of the user can be collected through the front-facing camera arranged on the first terminal, and the desktop image of the desktop where the first terminal is located can be collected through the overlooking camera arranged on the first terminal. The overlooking camera can acquire a desktop image of the desktop in an oblique downward 45-degree angle direction.
In S102, the posture category of the user is determined according to the upper body image and the desktop image of the user.
In the present disclosure, the posture category is one of off-table, lying on the stomach, head-bending, head-lowering, body-leaning, correct sitting. Wherein, the inclined body comprises a left side body, a right side body, a high shoulder, a low shoulder and the like. In addition, lying prone, head bending, head lowering, and body leaning are all bad sitting postures.
In S103, if the posture type is the poor sitting posture, a first prompt message is generated.
In the present disclosure, the first prompting message may be a message in a voice form, a message in a text form, a message in a vibration form, or the like.
In the technical scheme, after the upper half body image of the user and the desktop image of the desktop where the first terminal is located are obtained, the posture category of the user is determined according to the upper half body image of the user and the desktop image; and if the posture category is the poor sitting posture, generating a first prompt message. Like this, the user can in time correct the position of sitting through first suggestion message, has reduced because bad position of sitting leads to near-sighted risk and probability to can avoid the user because of the waist back damage, the muscle strain scheduling problem that bad position of sitting leads to. In addition, when the gesture category of the user is determined, the upper body image of the user and the desktop image are considered simultaneously, so that the detection precision of the gesture category is improved, and the trouble of the user caused by the fact that the first prompt message is generated by mistake is avoided.
A detailed description will be given below of a specific embodiment of determining the posture category of the user from the upper body image and the desktop image in S102.
In one embodiment, first, the upper body image and the desktop image are directly spliced; then, calculating human body parameters in the image obtained by direct splicing; and finally, matching the human body parameters with preset sitting posture parameters, and determining the posture category of the user according to the matching degree.
In another embodiment, the upper body image and the desktop image of the user are input into a pre-trained gesture detection model to obtain the gesture category of the user. The posture detection model can be obtained by training through learning various posture categories, particularly the prior information of bad sitting postures, and is strong in robustness, so that the detection accuracy of the posture categories of the user can be guaranteed even if the first terminal moves.
Specifically, the gesture detection model described above may determine the gesture category of the user by: after the upper body image and the desktop image of the user are input into the posture detection model, the upper body image and the desktop image can be respectively clipped, and then the clipped upper body image and the clipped desktop image are spliced; and finally, determining the posture category of the user according to the spliced images. The upper body image and the desktop image are spliced after being clipped, so that the spliced images are perfectly and naturally connected at the spliced position, and the detection precision of the posture category of the user is further improved.
Accordingly, as shown in fig. 2, the posture detection model includes: the clipping module is used for clipping the upper body image and the desktop image respectively; the splicing module is used for splicing the upper body image obtained after the clipping module clips and the desktop image obtained after clipping; and the category determining module is used for determining the gesture category of the user according to the image spliced by the splicing module.
In addition, in order to further protect the eyesight of the user, the first terminal can prompt the user when the user is in an out-of-sitting posture, and can also prompt the user when the distance between the eyes of the user and the desktop is short. Specifically, as shown in fig. 3, the method further includes S104 and S105.
In S104, a first target distance between the user' S eyes and the desktop is determined from the upper body image of the user.
First body image that can gather through the monocular camera to directly determine the first target distance between user's eyes and the desktop according to this first body image, and need not degree of depth camera or two mesh cameras, thereby reduced first terminal cost.
In S105, if the first target distance is smaller than the preset distance threshold, a second prompt message is generated.
In the present disclosure, the second prompting message may be a message in a voice form, a message in a text form, a message in a vibration form, or the like. Preferably, the second prompting message is in a different form from the first prompting message, so that the user can conveniently determine the problem in time according to the prompting message.
A detailed description will be given below of a specific embodiment of determining the first target distance between the eyes of the user and the desktop from the upper body image in S104. Specifically, the method can be realized by the following steps (1) and (2):
(1) A second target distance between the eye and the image capture device for capturing the upper body image is determined from the upper body image of the user.
(2) And determining the first target distance according to the second target distance, the position of the face in the upper half body image and the position information of the image acquisition device relative to the desktop.
A detailed description will be given below of a specific embodiment of determining the second target distance between the eye and the image capturing device for capturing the upper body image based on the upper body image in the step (1) above. Specifically, the second target distance may be determined by the following method, and in one embodiment, the method may be implemented by the following steps (11) to (13):
(11) And detecting the key points of the face of the upper body image of the user.
In the present disclosure, the face key point detection is also referred to as face key point positioning or face alignment, and refers to positioning key region positions of a face, including eyebrows, eyes, nose, mouth, face contour, etc., of a given face image. Illustratively, the face keypoints in the upper body image of the user may be extracted by an Active Shape Model (ASM) Model, an Active Appearance Model (AAM), or the like.
(12) And determining the binocular distance in the upper half body image based on the detection result of the face key points.
In the present disclosure, the binocular distance is a distance between two eyes in the upper body image of the user, and a distance between center points of the two eyes detected by the face key points may be taken as the binocular distance.
(13) And determining a second target distance according to the binocular distance in the upper half body image.
Specifically, a second reference distance between the eyes of the user and the image acquisition device may be determined according to the binocular distance in the upper body image of the user, the reference binocular distance in the reference upper body image of the user, and the first reference distance; and then, taking the second reference distance as a second target distance. The first reference distance is a distance between the eyes and the image acquisition device when the image acquisition device acquires a reference upper body image of the user, the reference upper body image is acquired in advance, and the reference binocular distance and the first reference distance are known quantities determined in advance.
For example, the second reference distance between the user's eye and the image capturing device may be determined by the following equation (1) according to the binocular distance in the user's upper body image, the reference binocular distance in the user's reference upper body image, and the first reference distance:
wherein L is 1c Is a first reference distance; l is 2c Is a second reference distance; l is 1m A reference binocular distance in the reference upper body image for the user; l is 2m Is the binocular distance in the user's upper body image.
In another embodiment, the method can be implemented by the following steps 11) to 13):
11 Face keypoint detection is performed on the user's upper body image.
12 Based on the face keypoint detection result, the face area in the upper body image is determined.
In the present disclosure, the face area is the size of the face area in the upper body image of the user, and the convex hull area of the face contour detected by the face key point may be used as the face area in the upper body image of the user.
13 A second target distance is determined based on the face area in the upper body image of the user.
Specifically, a third reference distance between the eyes of the user and the image acquisition device may be determined according to the face area in the upper body image of the user, the reference face area in the reference upper body image of the user, and the first reference distance; and then, taking the third reference distance as a second target distance. Wherein, the reference face area is a known quantity determined in advance.
For example, the third reference distance between the eyes of the user and the image capturing device may be determined by the following equation (2) according to the face area in the upper body image of the user, the reference face area in the reference upper body image of the user, and the first reference distance:
wherein L is 3c Is a third reference distance; s. the 1m A reference face area in a reference upper body image for the user; s 2m The face area in the user's upper body image.
In still another embodiment, the method can be realized by the following steps [11] to [13 ]:
[11] and detecting the key points of the face of the upper body image of the user.
[12] And determining the binocular distance and the face area in the upper half body image based on the face key point detection result.
[13] And determining a second target distance according to the binocular distance in the upper half image and the face area in the upper half image.
Specifically, a second reference distance between the eyes of the user and the image acquisition device may be determined according to the binocular distance in the upper body image of the user, the reference binocular distance in the reference upper body image of the user, and the first reference distance; meanwhile, determining a third reference distance between the eyes of the user and the image acquisition device according to the face area in the upper body image of the user, the reference face area in the reference upper body image of the user and the first reference distance; and then, determining a second target distance according to the second reference distance and the third reference distance. For example, an average value of the second reference distance and the third reference distance may be used as the second target distance.
In this embodiment, the second target distance between the eyes of the user and the image capturing device for capturing the upper body image is determined jointly according to the binocular distance and the face area in the upper body image of the user, so that the calculation accuracy of the second target distance can be improved, and the calculation accuracy of the first target distance between the eyes of the subsequent user and the desktop can be improved.
A detailed description is given below of a specific implementation manner of determining the first target distance according to the second target distance, the position of the face in the upper-half body image, and the position information of the image acquisition device relative to the desktop in step (2). Specifically, it can be realized by the following steps (21) and (22):
(21) And determining the coordinates of the eyes in the coordinate system corresponding to the image acquisition device according to the second target distance and the position of the face in the upper half body image.
Specifically, the ray direction from the origin of the coordinate system corresponding to the image acquisition device to the eyes of the user can be determined according to the position of the face in the upper half body image of the user; and then, determining the coordinates of the eyes of the user in a coordinate system corresponding to the image acquisition device according to the ray direction and the second target distance. The eye of the user can be regarded as a point, and the coordinate of the eye of the user in the coordinate system corresponding to the image acquisition device is the coordinate of a point in the coordinate system. The image acquisition device can be used as an origin, a central line of a shooting angle of the image acquisition device is used as an x axis, a horizontal direction is used as a y axis, and a vertical direction is used as a z axis to construct a coordinate system corresponding to the image acquisition device.
For example, the coordinates (x) of the eye in the coordinate system corresponding to the image capturing device may be determined by the following equation (3) according to the ray direction and the second target distance 1 ,y 1 ,z 1 )：
Wherein (x) d ,y d ,z d ) Is the ray direction, which is the unit vector; d is the second target distance.
(22) And determining the first target distance according to the coordinates of the eyes in the coordinate system corresponding to the image acquisition device and the position information of the image acquisition device relative to the desktop.
In the present disclosure, the position information of the image capturing device relative to the desktop includes a distance between the image capturing device and the desktop, and an inclination angle of the image capturing device relative to the desktop.
For example, the first target distance may be determined by the following equation (4) according to the coordinates of the eye in the coordinate system corresponding to the image capturing device and the position information of the image capturing device relative to the desktop:
d'＝z 1 ·cosθ+x 1 ·sinθ+h (4)
wherein d' is a first target distance; theta is the inclination angle of the image acquisition device relative to the desktop; h is the distance between the image acquisition device and the desktop.
In the embodiment, the distance between the eyes of the user and the desktop can be determined through simple geometric calculation, so that the calculation speed is high, and the calculation accuracy is high.
Further, as shown in fig. 4, the above method may further include the following steps S106 and S107.
In S106, it is determined whether the generation frequency of the first alert message reaches a preset frequency threshold within a preset time period.
In the present disclosure, if the generation frequency of the first reminding message does not reach the preset frequency threshold within the preset time period, returning to the step S101 to continue the execution until the generation frequency of the first reminding message reaches the preset frequency threshold within the preset time period; if the generation times of the first reminding message reach the preset time threshold value within the preset time length, it indicates that the user is unsuccessfully prompted for multiple times, and at this time, the user may be prompted in other ways, that is, S107 is executed.
In S107, a third prompting message is transmitted to a second terminal communicatively connected to the first terminal.
In this disclosure, the second terminal with first terminal communication connection can be user's own intelligent terminal (for example, smart mobile phone, panel computer, intelligent wearing equipment etc.), and when the user was the minors, the second terminal with first terminal communication connection can be its guardian or mr's intelligent terminal to can intervene or supervise it to correct the position of sitting through father and mother or mr, in order to avoid influencing eyesight because of bad position of sitting.
Based on the same inventive concept, the present disclosure also provides an information detecting apparatus, wherein the apparatus is applied to a first terminal. As shown in fig. 5, the information detection apparatus 500 includes: an obtaining module 501, configured to obtain an upper body image of a user and a desktop image of a desktop where the first terminal is located; a determining module 502, configured to determine a gesture category of the user according to the upper body image and the desktop image acquired by the acquiring module 501; a prompting module 503, configured to generate a first prompting message if the posture category determined by the determining module 502 is an out-of-position sitting posture.
In the technical scheme, after the upper half body image of the user and the desktop image of the desktop where the first terminal is located are obtained, the posture category of the user is determined according to the upper half body image of the user and the desktop image; and if the posture category is the poor sitting posture, generating a first prompt message. Like this, the user can in time correct the position of sitting through first suggestion message, has reduced because bad position of sitting leads to near-sighted risk and probability to can avoid the user because of the waist back damage, the muscle strain scheduling problem that bad position of sitting leads to. In addition, when the gesture category of the user is determined, the upper body image of the user and the desktop image are considered simultaneously, so that the detection precision of the gesture category is improved, and the trouble of the user caused by the fact that the first prompt message is generated by mistake is avoided.
Optionally, the determining module 502 is configured to: directly splicing the upper half body image and the desktop image; calculating human body parameters in the image obtained after direct splicing; and matching the human body parameters with preset sitting posture parameters, and determining the posture category of the user according to the matching degree.
Optionally, the determining module 502 is configured to input the upper body image and the desktop image into a pre-trained gesture detection model to obtain the gesture category of the user.
Optionally, the determining module 502 includes:
the clipping sub-module is used for respectively clipping the upper body image and the desktop image;
the splicing submodule is used for splicing the upper body image obtained after the clipping submodule clips and the desktop image obtained after clipping;
and the category determining submodule is used for determining the gesture category of the user according to the image spliced by the splicing submodule.
Optionally, the apparatus 500 further comprises:
a second determining module for determining a first target distance between the user's eyes and the desktop according to the upper body image;
the prompting module 503 is further configured to generate a second prompting message if the first target distance is smaller than a preset distance threshold.
Optionally, the second determining module includes:
the first determining sub-module is used for determining a second target distance between the eyes and an image acquisition device for acquiring the upper half body image according to the upper half body image;
and the second determining submodule is used for determining the first target distance according to the second target distance, the position of the face in the upper half body image and the position information of the image acquisition device relative to the desktop.
Optionally, the first determining sub-module includes:
the detection submodule is used for detecting the key points of the face of the upper half body image;
the third determining submodule is used for determining the binocular distance and/or the face area in the upper half body image based on the detection result of the face key point;
and the fourth determining submodule is used for determining the second target distance according to the binocular distance in the upper half body image and/or the face area in the upper half body image.
Optionally, the fourth determining submodule includes:
a fifth determining sub-module, configured to determine a second reference distance between the eye and the image capturing device according to the binocular distance in the upper body image, the reference binocular distance in the reference upper body image of the user, and a first reference distance, where the first reference distance is a distance between the eye and the image capturing device when the reference upper body image is captured; and/or a sixth determining submodule, configured to determine a third reference distance between the eye and the image acquisition device according to the face area in the upper body image, the reference face area in the reference upper body image, and the first reference distance;
a seventh determining submodule, configured to determine the second target distance according to the second reference distance and/or the third reference distance.
Optionally, the second determining sub-module includes:
the eighth determining submodule is used for determining the coordinates of the eyes in the coordinate system corresponding to the image acquisition device according to the second target distance and the position of the face in the upper half body image;
and the ninth determining submodule is used for determining the first target distance according to the coordinates and the position information of the image acquisition device relative to the desktop.
Optionally, the apparatus 500 further comprises:
the triggering module is configured to trigger the obtaining module 501 to obtain the upper body image of the user and the desktop image of the desktop where the first terminal is located until the generation frequency of the first reminding message reaches a preset frequency threshold within a preset time period;
and the sending module is used for sending a third prompt message to a second terminal in communication connection with the first terminal if the generation times reach the preset time threshold value within the preset time length.
Referring now to FIG. 6, a block diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), a desk lamp, and the like, and fixed terminals such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some implementations, the electronic devices may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located; determining the gesture category of the user according to the upper body image and the desktop image; and if the posture category is the bad sitting posture, generating a first prompt message.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module does not in some cases form a limitation on the module itself, for example, the acquiring module may also be described as a "module that acquires an image of the upper body of the user and an image of a desktop on which the first terminal is located".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Example 1 provides an information detection method applied to a first terminal, including obtaining an upper body image of a user and a desktop image of a desktop on which the first terminal is located; determining the gesture category of the user according to the upper body image and the desktop image; and if the posture category is the bad sitting posture, generating a first prompt message.
Example 2 provides the method of example 1, the determining the gesture category of the user from the upper torso image and the desktop image, comprising: and inputting the upper half body image and the desktop image into a pre-trained posture detection model to obtain the posture category of the user.
Example 3 provides the method of example 2, wherein inputting the upper body image and the desktop image into a pre-trained pose detection model to obtain a pose category of the user, comprises: respectively editing the upper body image and the desktop image; splicing the upper body image obtained after the clipping and the desktop image obtained after the clipping; and determining the gesture category of the user according to the spliced images.
Example 4 provides the method of any one of examples 1-3, further including, in accordance with one or more embodiments of the present disclosure: determining a first target distance between the user's eyes and the desktop according to the upper body image; and if the first target distance is smaller than a preset distance threshold, generating a second prompt message.
Example 5 provides the method of example 4, the determining a first target distance between the user's eyes and the desktop from the upper torso image, comprising: determining a second target distance between the eye and an image acquisition device for acquiring the upper body image according to the upper body image; and determining the first target distance according to the second target distance, the position of the face in the upper half body image and the position information of the image acquisition device relative to the desktop.
Example 6 provides the method of example 5, the determining, from the upper body image, a second target distance between the eye and an image capture device used to capture the upper body image, comprising: detecting key points of the human face of the upper half body image; determining binocular distance and/or face area in the upper half body image based on the face key point detection result; and determining the second target distance according to the binocular distance in the upper body image and/or the face area in the upper body image.
Example 7 provides the method of example 6, the determining the second target distance according to the binocular distance in the upper body image and/or the face area in the upper body image, including: determining a second reference distance between the eyes and the image acquisition device according to the binocular distance in the upper body image, the reference binocular distance in the reference upper body image of the user and a first reference distance, wherein the first reference distance is a distance between the eyes and the image acquisition device when the reference upper body image is acquired; and/or determining a third reference distance between the eyes and the image acquisition device according to the face area in the upper body image, the reference face area in the reference upper body image and the first reference distance; and determining the second target distance according to the second reference distance and/or the third reference distance.
Example 8 provides the method of example 5, the determining the first target distance according to the second target distance, the position of the face in the upper-body image, and the position information of the image acquisition apparatus relative to the desktop, including: determining the coordinates of the eyes in a coordinate system corresponding to the image acquisition device according to the second target distance and the position of the face in the upper half body image; and determining the first target distance according to the coordinates and the position information of the image acquisition device relative to the desktop.
Example 9 provides the method of any one of examples 1-3, further comprising, in accordance with one or more embodiments of the present disclosure: returning to the step of acquiring the upper body image of the user and the desktop image of the desktop where the first terminal is located until the generation frequency of the first reminding message reaches a preset frequency threshold value within a preset time length; and if the generation times reach the preset time threshold within the preset time, sending a third prompt message to a second terminal in communication connection with the first terminal.
Example 10 provides, in accordance with one or more embodiments of the present disclosure, an information detecting apparatus applied to a first terminal, including: the acquisition module is used for acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located; the determining module is used for determining the gesture category of the user according to the upper body image and the desktop image acquired by the acquiring module; and the prompting module is used for generating a first prompting message if the posture category determined by the determining module is an undesirable sitting posture.
Example 11 provides a computer-readable medium, on which is stored a computer program that, when executed by a processing device, implements the steps of the method of any of examples 1-9, in accordance with one or more embodiments of the present disclosure.
Example 12 provides, in accordance with one or more embodiments of the present disclosure, an electronic device, comprising: a storage device having a computer program stored thereon; processing means for executing the computer program in the storage means to implement the steps of the method of: acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located; determining the gesture category of the user according to the upper body image and the desktop image; and if the posture category is the bad sitting posture, generating a first prompt message.
Example 13 provides the electronic device of example 12, the determining the gesture category of the user from the upper torso image and the desktop image, comprising, in accordance with one or more embodiments of the present disclosure: and inputting the upper half body image and the desktop image into a pre-trained posture detection model to obtain the posture category of the user.
Example 14 provides the electronic device of example 13, wherein the inputting the upper body image and the desktop image into a pre-trained pose detection model to obtain the pose category of the user includes: respectively editing the upper body image and the desktop image; splicing the upper body image obtained after the cutting and the desktop image obtained after the cutting; and determining the gesture category of the user according to the spliced images.
Example 15 provides the electronic device of any one of examples 12-14, the method further including, in accordance with one or more embodiments of the present disclosure: determining a first target distance between the user's eyes and the desktop according to the upper body image; and if the first target distance is smaller than a preset distance threshold, generating a second prompt message.
Example 16 provides the electronic device of example 15, the determining a first target distance between the user's eyes and the desktop from the upper torso image, comprising, in accordance with one or more embodiments of the present disclosure: determining a second target distance between the eye and an image acquisition device for acquiring the upper body image according to the upper body image; and determining the first target distance according to the second target distance, the position of the face in the upper half body image and the position information of the image acquisition device relative to the desktop.
Example 17 provides the electronic device of example 16, wherein determining, from the upper torso image, a second target distance between the eye and an image capture device used to capture the upper torso image includes: detecting key points of the human face of the upper half body image; determining binocular distance and/or face area in the upper half body image based on the face key point detection result; and determining the second target distance according to the binocular distance in the upper body image and/or the face area in the upper body image.
Example 18 provides the electronic device of example 17, the determining the second target distance according to binocular distances in the upper body image and/or face areas in the upper body image, including: determining a second reference distance between the eyes and the image acquisition device according to the binocular distance in the upper body image, the reference binocular distance in the reference upper body image of the user and a first reference distance, wherein the first reference distance is a distance between the eyes and the image acquisition device when the reference upper body image is acquired; and/or determining a third reference distance between the eyes and the image acquisition device according to the face area in the upper body image, the reference face area in the reference upper body image and the first reference distance; and determining the second target distance according to the second reference distance and/or the third reference distance.
Example 19 provides the electronic device of example 16, the determining the first target distance according to the second target distance, the position of the face in the upper-body image, and the position information of the image acquisition apparatus relative to the desktop, including: determining the coordinates of the eyes in a coordinate system corresponding to the image acquisition device according to the second target distance and the position of the face in the upper half body image; and determining the first target distance according to the coordinates and the position information of the image acquisition device relative to the desktop.
Example 20 provides the electronic device of any one of examples 12-14, the method further comprising, in accordance with one or more embodiments of the present disclosure: returning to the step of acquiring the upper body image of the user and the desktop image of the desktop where the first terminal is located until the generation frequency of the first reminding message reaches a preset frequency threshold value within a preset time length; and if the generation times reach the preset time threshold within the preset time, sending a third prompt message to a second terminal in communication connection with the first terminal.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other combinations of features described above or equivalents thereof without departing from the spirit of the disclosure. For example, the above features and the technical features disclosed in the present disclosure (but not limited to) having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Claims (12)
1. An information detection method is applied to a first terminal, and is characterized by comprising the following steps:
acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located;
determining the gesture category of the user according to the upper body image and the desktop image;
and if the posture category is the bad sitting posture, generating a first prompt message.
2. The method of claim 1, wherein determining the gesture category of the user from the upper torso image and the desktop image comprises:
and inputting the upper half body image and the desktop image into a pre-trained posture detection model to obtain the posture category of the user.
3. The method of claim 2, wherein the inputting the upper body image and the desktop image into a pre-trained pose detection model to obtain the pose category of the user comprises:
respectively editing the upper body image and the desktop image;
splicing the upper body image obtained after the cutting and the desktop image obtained after the cutting;
and determining the gesture category of the user according to the spliced images.
4. The method according to any one of claims 1-3, further comprising:
determining a first target distance between the user's eyes and the desktop according to the upper body image;
and if the first target distance is smaller than a preset distance threshold, generating a second prompt message.
5. The method of claim 4, wherein determining a first target distance between the user's eyes and the desktop from the upper torso image comprises:
determining a second target distance between the eye and an image acquisition device for acquiring the upper body image according to the upper body image;
and determining the first target distance according to the second target distance, the position of the face in the upper half body image and the position information of the image acquisition device relative to the desktop.
6. The method of claim 5, wherein determining a second target distance between the eye and an image capture device used to capture the upper body image from the upper body image comprises:
detecting key points of the face of the upper half body image;
determining binocular distance and/or face area in the upper half body image based on the face key point detection result;
and determining the second target distance according to the binocular distance in the upper body image and/or the face area in the upper body image.
7. The method of claim 6, wherein determining the second target distance based on the binocular distance in the upper body image and/or the face area in the upper body image comprises:
determining a second reference distance between the eyes and the image acquisition device according to the binocular distance in the upper body image, the reference binocular distance in the reference upper body image of the user and a first reference distance, wherein the first reference distance is a distance between the eyes and the image acquisition device when the reference upper body image is acquired; and/or determining a third reference distance between the eyes and the image acquisition device according to the face area in the upper body image, the reference face area in the reference upper body image and the first reference distance;
and determining the second target distance according to the second reference distance and/or the third reference distance.
8. The method of claim 5, wherein determining the first target distance according to the second target distance, the position of the face in the upper-half image, and the position information of the image acquisition device relative to the desktop comprises:
determining the coordinates of the eyes in a coordinate system corresponding to the image acquisition device according to the second target distance and the position of the face in the upper half body image;
and determining the first target distance according to the coordinates and the position information of the image acquisition device relative to the desktop.
9. The method according to any one of claims 1-3, further comprising:
returning to the step of acquiring the upper body image of the user and the desktop image of the desktop where the first terminal is located until the generation frequency of the first reminding message reaches a preset frequency threshold value within a preset time length;
and if the generation times reach the preset time threshold value within the preset time length, sending a third prompt message to a second terminal in communication connection with the first terminal.
10. An information detection device applied to a first terminal, comprising:
the acquisition module is used for acquiring an upper body image of a user and a desktop image of a desktop where the first terminal is located;
the determining module is used for determining the gesture category of the user according to the upper body image and the desktop image acquired by the acquiring module;
and the prompting module is used for generating a first prompting message if the posture category determined by the determining module is an undesirable sitting posture.
11. A computer-readable medium, on which a computer program is stored which, when being executed by a processing means, carries out the steps of the method according to any one of claims 1-9.
12. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method according to any one of claims 1 to 9.
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| CN202110833196.2A CN115690893A (en) | 2021-07-22 | 2021-07-22 | Information detection method, information detection device, information detection medium, and electronic device |
| PCT/CN2022/096832 WO2023000838A1 (en) | 2021-07-22 | 2022-06-02 | Information detection method and apparatus, medium, and electronic device |
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| CN202110833196.2A CN115690893A (en) | 2021-07-22 | 2021-07-22 | Information detection method, information detection device, information detection medium, and electronic device |
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| US8442267B2 (en) * | 2008-12-24 | 2013-05-14 | Electronics And Telecommunications Research Institute | Apparatus and method for detecting upper body posture and hand posture |
| CN103908064A (en) * | 2014-04-03 | 2014-07-09 | 安徽海聚信息科技有限责任公司 | Intelligent desk with sitting posture correcting function and correcting method implemented by intelligent desk |
| CN110290349B (en) * | 2019-06-17 | 2022-03-08 | 苏州佳世达电通有限公司 | Lamp and method for detecting sitting posture state of user |
| CN110287864A (en) * | 2019-06-24 | 2019-09-27 | 火石信科(广州)科技有限公司 | A kind of intelligent identification of read-write scene read-write element |
| CN111626211B (en) * | 2020-05-27 | 2023-09-26 | 大连成者云软件有限公司 | Sitting posture identification method based on monocular video image sequence |
| CN111814718A (en) * | 2020-07-17 | 2020-10-23 | 浙江工业大学 | Attention detection method integrating multiple discrimination technologies |
| CN112617398A (en) * | 2020-12-30 | 2021-04-09 | 甄昊元 | Self-adaptive adjusting table and table control system |
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