CN109542219B - Gesture interaction system and method applied to intelligent classroom - Google Patents
Gesture interaction system and method applied to intelligent classroom Download PDFInfo
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- CN109542219B CN109542219B CN201811232334.6A CN201811232334A CN109542219B CN 109542219 B CN109542219 B CN 109542219B CN 201811232334 A CN201811232334 A CN 201811232334A CN 109542219 B CN109542219 B CN 109542219B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
Abstract
The invention relates to the field of human-computer interaction, and particularly discloses a gesture interaction system and method applied to an intelligent classroom, which comprises the following steps: the camera module is used for collecting face images, hand images and posture images of the user; the storage module is used for storing the acquired images; the processing module is used for identifying the face in the acquired image based on a face identification algorithm, matching the identified face with a white list face library and judging whether the user belongs to a white list user or not; if yes, identifying the hand gesture of the white list user in the acquired image, and matching the hand gesture with a pre-stored gesture sample library; and judging whether the white list user gesture accords with a pre-stored gesture sample, and if so, executing the operation corresponding to the accorded gesture sample. By adopting the technical scheme of the invention, real-time authorization can be realized.
Description
Technical Field
The invention relates to the field of human-computer interaction, in particular to a gesture interaction system and method applied to an intelligent classroom.
Background
Intelligent classrooms, also known as smart classrooms, are a form of digital classroom and future classrooms. The intelligent classroom is a novel education form, which is different from the traditional lecture teaching mode; original multimedia equipment of intelligence classroom construction make full use of adopts liquid crystal touch-control all-in-one or projector, whiteboard to show the full mutual wisdom terminal of scheme cooperation, and control, audio amplifier, power amplifier, wireless public address, teaching computer, a plurality of systems such as interactive tablet software have been embraced to terminal single unit equipment, and each high integration of system and seamless combination can realize: the method comprises the steps of wireless screen projection display, grouping interactive teaching, remote video conference, teacher-student interactive classroom, automatic recording live broadcast and the like.
When the existing intelligent classroom controls the full-interactive intelligent terminal, the control is realized by means of APP on intelligent mobile equipment or intelligent center control is clicked by hands; the teacher is convenient to operate when giving lessons on the platform, but when the teacher walks to the middle of the student for giving lessons, the teacher needs to return to the platform for intelligent center control operation or bring the intelligent mobile equipment at any time, and the operation is realized through the APP on the intelligent mobile equipment; the use is inconvenient in the practical teaching.
For convenience of teacher operation, a recognition device interacting through gestures needs to be added to realize the operation and control of a full-interactive smart terminal, for example, a Kinect body sensor of microsoft. Because the number of people in the classroom is many, if do not restrict the operation authority, mr's gesture and student's gesture mutual interference easily can lead to the system can't correctly discern mr's interactive gesture. In order to improve the accuracy of identification, the teacher can only be given the operation authority and only effective gestures sent by the teacher are identified; however, when students need to perform related operations in part of teaching scenes, the system cannot identify effective gestures of the students; for example, when a teacher A draws a certain student B to explain his composition, the student B needs to turn pages after finishing speaking one page, and at the moment, the teacher A needs to be helped to operate or the student B needs to operate on a platform, so that the teaching experience is not good.
Therefore, a system and a method for gesture interaction capable of real-time authorization are needed.
Disclosure of Invention
The invention aims to provide a gesture interaction system and method applied to an intelligent classroom, which can realize real-time authorization.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a gesture interaction system applied to a smart classroom, comprising:
the camera module is used for collecting face images, hand images and posture images of the user and uploading the collected images;
the storage module is used for storing the acquired images; the storage module is also prestored with a face recognition algorithm, a gesture recognition model, a gesture sample library, a white list face library and a temporary authorized face library; different gesture samples in the gesture sample library correspond to different operations;
the processing module is used for calling the images, the face recognition algorithm, the posture recognition model, the gesture sample library and the white list face library in the storage module; identifying the face in the collected image based on a face identification algorithm, matching the identified face with a white list face library, and judging whether the user belongs to a white list user; if yes, identifying the hand gesture of the white list user in the acquired image, and matching the hand gesture with a pre-stored gesture sample library; judging whether the white list user gesture accords with a pre-stored gesture sample, if so, executing the operation corresponding to the conformed gesture sample;
the gesture sample library comprises authorization gestures, if a white list user uses the authorization gestures to any user alpha, the processing module identifies the gesture of the user alpha in the acquired image based on the gesture identification model, and then judges whether the identified gesture belongs to a standing gesture or not; if yes, the face of the user alpha is added into a temporary authorized face library in the storage module, and the gesture of the user alpha is recognized.
The basic scheme principle and the beneficial effects are as follows:
1. and a white list face library is set, in actual use, after the face of the teacher is input into the white list face library, the face of the teacher can be directly interactively operated after being successfully matched with the white list face library, extra authorization is not needed, and the use is convenient. The white list face library only authorizes teachers, so that the gestures of students are prevented from being recognized by mistake when the action amplitude of the students in a classroom is large, wrong operation is executed, interference is avoided, and meanwhile, the safety is good;
2. authorizing any user alpha by a white list user through an authorization gesture to enable the user alpha to become a temporary authorization user; in actual use, a teacher can authorize any student through an authorization gesture, so that the student becomes a temporary authorization user, a teacher and a student can both become operators of an intelligent classroom, and more teaching needs can be met compared with the case that only the teacher is identified; compared with the method that the students and teachers do not need to be authorized and do not have different identification, the unauthorized operation of the students can be avoided;
3. when the authorized gesture is actually set, unless the teacher makes physical contact with the students through the gesture, the non-contact directional gesture points to the students in a certain range, and when the teacher specifically authorizes the students, the error is large; through double confirmation of authorized gestures and standing gestures, the recognition error can be reduced, and the recognition accuracy is improved; moreover, in practical teaching, students usually listen and speak while sitting; when any student stands, the student usually has an interactive state with the teacher, for example, the student stands up to answer the question of the teacher, and the student usually needs to operate at the moment.
4. Compared with the situation that the user alpha directly obtains the authority through gestures, the user alpha is granted through the white list user, in the actual teaching, the operation of the student after randomly obtaining the authority is avoided, and the safety can be improved.
5. The basic scheme can meet the control requirements of multiple users, particularly meet the control authority distribution requirements when the multiple users control, so that the interactive control range of gesture recognition is expanded, and the method is simple, real-time and easy for engineering application.
Further, when the processing module identifies gestures in the acquired images, the acquired images are converted into image frames, hands are positioned from the image frames through background modeling, moving object modeling and an HSV skin color detection model, then the movement tracks of the hands are obtained through a particle filtering algorithm of an HSV histogram, and the movement tracks of the hands are used as the gestures.
The particle filter algorithm has strong modeling capability on the nonlinear characteristic of the human hand motion track in the scheme, and can accurately predict the human hand motion track.
Further, if the pose of the user α becomes sitting, the processing module removes the face of the user α from the temporary authorized faces library.
In practical teaching, when a student changes from a standing posture to a sitting posture, the interaction with a teacher is usually finished, for example, the answer to a question is finished; at the moment, the student enters the listening and speaking state again, and operation is not needed any more; the authority is withdrawn in time, so that the condition that the student can operate without permission of the teacher due to long-term authorization can be avoided.
Further, the gesture sample library also comprises an authorization confirmation gesture, after the white list user uses the authorization gesture to any user alpha, the processing module also judges whether the user alpha uses the authorization confirmation gesture, and if so, the face of the user alpha is added to the temporary authorized face library.
The accuracy of the alpha judgment of the user can be further improved by the triple verification of the authorization gesture, the standing gesture and the confirmation gesture.
Further, the identified hand portion includes an arm and a finger.
Compared with the fingers which can recognize the human hand independently, the hand gesture made by combining the arm and the finger is more, and the operation which can be realized is more.
Further, the camera module comprises a depth camera.
The depth camera has three-dimensional sensing and three-dimensional modeling capabilities, and compared with a common camera which can only shoot plane images, the depth camera can also obtain depth information of shot objects, namely three-dimensional position and size information, and is beneficial to subsequent recognition of human faces, human hands and postures.
A gesture interaction method applied to a smart classroom comprises the following steps:
s1, collecting the face, hand and posture images of the user;
s2, recognizing the face in the collected image, matching the recognized face with a white list face library, and judging whether the user belongs to a white list user;
s3, if the user belongs to the white list user, recognizing the hand gesture of the white list user in the collected image, and matching the hand gesture with a gesture sample library; judging whether the white list user gestures accord with pre-stored gesture samples or not;
s4, if the gesture sample accords with the pre-stored gesture sample, executing the operation corresponding to the accordant gesture sample;
s5, if the white list user uses an authorization gesture to any user alpha, recognizing the gesture of the user alpha in the acquired image, and judging whether the recognized gesture belongs to a standing gesture;
and S6, if the user alpha belongs to the standing posture, judging whether the user alpha uses the gesture for confirming the authorization, and if so, adding the face of the user alpha to a temporary authorized face library.
Authorizing any user alpha by a white list user through an authorization gesture to enable the user alpha to become a temporary authorization user; in actual use, a teacher can authorize any student through an authorization gesture, so that the student becomes a temporary authorization user, a teacher and a student can both become operators of an intelligent classroom, and more teaching needs can be met compared with the case that only the teacher is identified; compared with the method that the students and teachers do not need to be authorized and identified indiscriminately, the unauthorized operation of the students can be avoided.
Further, the S6 further includes: if the pose of the user alpha changes to a sitting position, the face of the user alpha is removed from the temporary authorized face library.
The authority is withdrawn in time, so that the operation of students without permission by teachers due to long-term authorization can be avoided. If the authority is not recovered, the condition that most students have the authority inevitably occurs after a period of time, and the condition that the gestures of the teacher and the students are mutually interfered occurs at the moment; after the student sits down, the authority is automatically withdrawn, no additional operation is needed, and the use is convenient.
Further, in S3, when the gesture of the human hand of the white list user in the acquired image is recognized, the acquired image is converted into an image frame, the human hand is positioned from the image frame through background modeling, moving object modeling and HSV skin color detection models, the movement trajectory of the human hand is obtained by using a particle filtering algorithm of an HSV histogram, and the movement trajectory of the human hand is used as the gesture.
The motion trail of the human hand can be accurately predicted by using a particle filter algorithm.
Further, in S3, the hand part to be recognized includes an arm and a finger.
Compared with the method of only recognizing fingers, the moving range of the arm is large, and formed gestures are easier to recognize.
Drawings
FIG. 1 is a flowchart of a second embodiment of a gesture interaction method applied to a smart classroom;
FIG. 2 is a flowchart illustrating two steps S5-S6 of an embodiment of a gesture interaction method applied to a smart classroom.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
A gesture interaction system applied to an intelligent classroom comprises a camera module, a storage module and a processing module.
The camera module comprises a depth camera which is used for collecting human faces, human hands and posture images of users and uploading the collected images to the storage module. The identified parts of the human hand include arms and fingers. In this embodiment, the 3D sensor of smart mark science and technology is adopted to dark camera, and this 3D sensor can effectively avoid the interference such as light change, curtain swing, glass reflection, back row personnel walk among the acquisition process.
The storage module is used for storing the acquired image; the storage module is also prestored with a gesture sample library, a white list face library, a temporary authorization face library, a face recognition algorithm based on a deep neural algorithm and a posture recognition model based on the deep neural algorithm; different gesture samples in the gesture sample library correspond to different operations, and the gesture sample library also comprises an authorization gesture and an authorization confirmation gesture; in the embodiment, the input of the white list face library can be uniformly input by an administrator, and can also be automatically input by a teacher through a mobile phone APP. In this embodiment, the storage module adopts western data HUS726T4TALA6L 44T enterprise hard disk.
The processing module is used for calling the images, the face recognition algorithm, the posture recognition model, the gesture sample library and the white list face library in the storage module; the processing module identifies the face in the acquired image based on a face identification algorithm of a deep neural algorithm, matches the identified face with a white list face library, and judges whether the user belongs to a white list user; if yes, recognizing the gestures of the white list user hands in the acquired image: the collected images are converted into image frames, the human hand is positioned from the image frames through background modeling, moving object modeling and an HSV skin color detection model, the moving track of the human hand is obtained through a particle filtering algorithm of an HSV histogram, and the moving track of the human hand is used as a gesture. Matching the obtained gesture with a gesture sample in a pre-stored gesture sample library; and judging whether the white list user gesture accords with a pre-stored gesture sample, and if so, executing the operation corresponding to the accorded gesture sample.
If the white list user uses the authorization gesture to any user α, in this embodiment, the authorization gesture is that the elbow joint of the white list user is straight, the index finger is straight, and the other fingers are bent, pointing to the user α with the index finger. The processing module identifies the postures of the users in the area pointed by the white list users based on a posture identification model of a deep neural algorithm, and judges whether the identified postures belong to standing postures. Regarding the identification method of the white list user pointing area, the patent document CN101344816B has been disclosed in detail, belongs to the prior art, and is not described here again. If the user belongs to the standing posture, the user is determined to be a user alpha, whether the user alpha uses the authorization determination gesture is judged, and if the user alpha uses the authorization determination gesture, the face of the user alpha is added into a temporary authorization face library; in this embodiment, the confirmation gesture is a general "OK" gesture; if the posture of the user alpha is changed from the standing posture to the sitting posture, the processing module removes the face of the user alpha from the temporary authorized face library. In this embodiment, the processing module is an intel I78700K processor.
For example, when a teacher A elbow joint of a white list user is straightened, an index finger is straightened, other fingers are bent, and the index finger points to a standing student B, the processing module matches the obtained gesture of the teacher A with the gesture samples in the gesture sample library in the storage module, the matching result is an authorized gesture, the processing module identifies the gesture of the user in the pointing area of the teacher A based on a gesture identification model of a deep neural algorithm, and at the moment, the processing module identifies the gesture of the student B and judges the gesture of the student B as a standing gesture; at the moment, the student B makes an 'OK' gesture, the depth camera collects the hand image of the student B, the collected image is uploaded to the storage module, the processing module calls a gesture sample library in the storage module, the processing module identifies the hand gesture of the student B, the identified 'OK' gesture is matched with the gesture sample in the gesture sample library, and after the 'OK' gesture is matched to be a confirmed authorization gesture, the face of the student B is added into the temporary authorized face library; and the processing module executes the operation corresponding to the gesture of the gesture sample conformed by the student B. When the processing module recognizes that the posture of the student B becomes the sitting posture, the processing module removes the student B from the temporary authorized face library.
Example two
As shown in fig. 1, a gesture interaction method applied to a smart classroom based on a gesture interaction system applied to the smart classroom includes the following steps:
s1, collecting the face, hand and posture images of the user;
s2, recognizing the face in the collected image, matching the recognized face with a white list face library, and judging whether the user belongs to a white list user;
s3, if the user belongs to the white list user, recognizing the gesture of the hand of the white list user in the collected image: converting the collected image into an image frame, positioning an arm and a finger of a hand from the image frame through background modeling, moving object modeling and an HSV skin color detection model, obtaining a motion track of the arm and the finger by using a particle filtering algorithm of an HSV histogram, and taking the motion track of the arm and the finger as a gesture; matching the gesture with a gesture sample library; judging whether the white list user gestures accord with pre-stored gesture samples or not;
s4, if the gesture sample accords with the pre-stored gesture sample, executing the operation corresponding to the accordant gesture sample;
as shown in fig. 2, S5, if the white list user uses an authorization gesture to any user α, recognizing the gesture of the user α in the acquired image, and determining whether the recognized gesture belongs to a standing gesture; in the embodiment, if the standing posture is not detected, the identification is performed again after 5S, and if the standing posture is still not detected, the identification is finished;
s6, if the user alpha belongs to the standing posture, whether the user alpha uses the authorization confirmation gesture is judged, and if the user alpha uses the authorization confirmation gesture, the face of the user alpha is added into a temporary authorization face library; if the posture of the user alpha is changed from the standing posture to the sitting posture, the face of the user alpha is removed from the temporary authorized face library. In this embodiment, if the confirmation gesture is not detected, the recognition is performed again after 5S, and if the confirmation gesture is still not detected, the recognition is ended.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. A gesture interaction system applied to a smart classroom, comprising:
the camera module is used for collecting face images, hand images and posture images of the user and uploading the collected images;
the storage module is used for storing the acquired images; the storage module is also prestored with a face recognition algorithm, a gesture recognition model, a gesture sample library, a white list face library and a temporary authorized face library; different gesture samples in the gesture sample library correspond to different operations;
the processing module is used for calling the images, the face recognition algorithm, the posture recognition model, the gesture sample library and the white list face library in the storage module; identifying the face in the collected image based on a face identification algorithm, matching the identified face with a white list face library, and judging whether the user belongs to a white list user; if yes, identifying the hand gesture of the white list user in the acquired image, and matching the hand gesture with a pre-stored gesture sample library; judging whether the white list user gesture accords with a pre-stored gesture sample, if so, executing the operation corresponding to the conformed gesture sample;
the gesture sample library comprises authorization gestures, if a white list user uses the authorization gestures to any user alpha, the processing module identifies the gesture of the user alpha in the acquired image based on the gesture identification model, and then judges whether the identified gesture belongs to a standing gesture or not; if yes, the face of the user alpha is added into a temporary authorized face library in the storage module, and the gesture of the user alpha is recognized.
2. The gesture interaction system applied to the smart classroom according to claim 1, wherein: when recognizing gestures in the collected images, the processing module converts the collected images into image frames, positions hands from the image frames through background modeling, moving object modeling and HSV skin color detection models, obtains movement tracks of the hands by utilizing a particle filtering algorithm of an HSV histogram, and takes the movement tracks of the hands as the gestures.
3. The gesture interaction system applied to the smart classroom according to claim 1, wherein: if the pose of the user alpha changes to a sitting position, the processing module removes the face of the user alpha from the temporary authorized face library.
4. The gesture interaction system applied to the smart classroom according to claim 3, wherein: the gesture sample library also comprises an authorization confirmation gesture, after the white list user uses the authorization gesture to any user alpha, the processing module also judges whether the user alpha uses the authorization confirmation gesture, and if so, the face of the user alpha is added to the temporary authorization face library.
5. The gesture interaction system applied to the smart classroom according to claim 1, wherein: the identified parts of the human hand include arms and fingers.
6. The gesture interaction system applied to the smart classroom according to claim 1, wherein: the camera module comprises a depth camera.
7. A gesture interaction method applied to an intelligent classroom is characterized by comprising the following steps:
s1, collecting the face, hand and posture images of the user;
s2, recognizing the face in the collected image, matching the recognized face with a white list face library, and judging whether the user belongs to a white list user;
s3, if the user belongs to the white list user, recognizing the hand gesture of the white list user in the collected image, and matching the hand gesture with a gesture sample library; judging whether the white list user gestures accord with pre-stored gesture samples or not;
s4, if the gesture sample accords with the pre-stored gesture sample, executing the operation corresponding to the accordant gesture sample;
s5, if the white list user uses an authorization gesture to any user alpha, recognizing the gesture of the user alpha in the acquired image, and judging whether the recognized gesture belongs to a standing gesture;
and S6, if the user alpha belongs to the standing posture, judging whether the user alpha uses the gesture for confirming the authorization, and if so, adding the face of the user alpha to a temporary authorized face library.
8. The gesture interaction method applied to the smart classroom according to claim 7, wherein: the S6 further includes: if the pose of the user alpha changes to a sitting position, the face of the user alpha is removed from the temporary authorized face library.
9. The gesture interaction method applied to the smart classroom according to claim 8, wherein: in the step S3, when the gesture of the hand of the white list user in the acquired image is recognized, the acquired image is converted into an image frame, the hand is positioned from the image frame through background modeling, moving object modeling and HSV skin color detection models, the movement trajectory of the hand is obtained by using the particle filtering algorithm of the HSV histogram, and the movement trajectory of the hand is used as the gesture.
10. The gesture interaction method applied to the smart classroom according to claim 7, wherein: in S3, the identified hand part includes an arm and a finger.
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| CN110611734A (en) * | 2019-08-08 | 2019-12-24 | 深圳传音控股股份有限公司 | Interaction method and terminal |
| CN113949936A (en) * | 2020-07-17 | 2022-01-18 | 华为技术有限公司 | Screen interaction method and device of electronic equipment |
| CN112799574A (en) * | 2021-02-23 | 2021-05-14 | 京东方科技集团股份有限公司 | Display control method and display device |
| CN113221712A (en) * | 2021-05-06 | 2021-08-06 | 新疆爱华盈通信息技术有限公司 | Method, device and equipment for detecting risks by recognizing gestures of people |
| CN114374791B (en) * | 2021-11-25 | 2022-09-13 | 慧之安信息技术股份有限公司 | Camera adjusting method and system based on gesture recognition |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101344816A (en) * | 2008-08-15 | 2009-01-14 | 华南理工大学 | Human-machine interaction method and device based on sight tracing and gesture discriminating |
| CN106022211A (en) * | 2016-05-04 | 2016-10-12 | 北京航空航天大学 | Method using gestures to control multimedia device |
| CN107504621A (en) * | 2017-07-07 | 2017-12-22 | 珠海格力电器股份有限公司 | Air-conditioning line control machine and its control method and control device |
| CN108105954A (en) * | 2017-11-07 | 2018-06-01 | 珠海格力电器股份有限公司 | Apparatus control method and device, storage medium and processor |
| CN108389578A (en) * | 2018-02-09 | 2018-08-10 | 深圳市鹰硕技术有限公司 | Smart classroom speech control system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5982917B2 (en) * | 2012-03-22 | 2016-08-31 | 株式会社リコー | Information processing apparatus, operation authority grant program, and projection system |
| US10166995B2 (en) * | 2016-01-08 | 2019-01-01 | Ford Global Technologies, Llc | System and method for feature activation via gesture recognition and voice command |
-
2018
- 2018-10-22 CN CN201811232334.6A patent/CN109542219B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101344816A (en) * | 2008-08-15 | 2009-01-14 | 华南理工大学 | Human-machine interaction method and device based on sight tracing and gesture discriminating |
| CN106022211A (en) * | 2016-05-04 | 2016-10-12 | 北京航空航天大学 | Method using gestures to control multimedia device |
| CN107504621A (en) * | 2017-07-07 | 2017-12-22 | 珠海格力电器股份有限公司 | Air-conditioning line control machine and its control method and control device |
| CN108105954A (en) * | 2017-11-07 | 2018-06-01 | 珠海格力电器股份有限公司 | Apparatus control method and device, storage medium and processor |
| CN108389578A (en) * | 2018-02-09 | 2018-08-10 | 深圳市鹰硕技术有限公司 | Smart classroom speech control system |
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