CN113095259A - Remote online course teaching management method - Google Patents

Remote online course teaching management method Download PDF

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CN113095259A
CN113095259A CN202110426371.6A CN202110426371A CN113095259A CN 113095259 A CN113095259 A CN 113095259A CN 202110426371 A CN202110426371 A CN 202110426371A CN 113095259 A CN113095259 A CN 113095259A
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崔炜
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Shanghai Squirrel Classroom Artificial Intelligence Technology Co Ltd
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Abstract

The embodiment of the invention discloses a remote online course teaching management method, belongs to the technical field of online teaching, and is used for solving the problem that the learning concentration condition of each student cannot be supervised in real time in the conventional remote online teaching. The method comprises the following steps: the online teaching student client collects face data of a currently logged student and identifies a student identifier corresponding to the face data; the on-line teaching student client acquires the head image of the current student according to a preset period and uploads the head image of the current student and the corresponding student identification to the remote server; the remote server carries out real-time image processing on each head image to obtain the lesson attending action information of each online student, determines the class attending concentration degree of each online student according to the lesson attending action information of each online student, sorts all online student identifications according to the class attending concentration degrees of all online students, and provides the sorting result to the client of the online teaching teacher. The invention can realize real-time supervision of the student listening state.

Description

Remote online course teaching management method
Technical Field
The invention belongs to the technical field of online teaching, and particularly relates to a remote online course teaching management method.
Background
With the development of computer technology, online network teaching has become an emerging teaching mode. In the current online teaching, students and teachers log in a server through own clients respectively, and the clients and the server are connected through a network to realize online teaching. Compared with an offline teaching mode, the remote online teaching mode has the advantages of being convenient and flexible to attend a class and saving time for students to come to and go from a school.
However, in the existing live-broadcast online teaching, generally, a student end can see a teaching live video of a teacher, but the teacher end cannot see or cannot see the student videos of a plurality of online student ends at the same time, so that the teacher cannot know whether the students are listening to the class seriously or dozing off or distracting, and the defects that the teaching classroom management is inconvenient and each student cannot be supervised to learn to concentrate on the situation in real time exist.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a remote online course teaching management method, which is used for solving the problems that the existing remote online teaching classroom management is inconvenient and the learning concentration condition of each student cannot be supervised in real time. The invention can realize the purpose of monitoring the student listening state in real time and improving the learning efficiency of the student.
The embodiment of the invention provides a remote online course teaching management method, which comprises the following steps:
the online teaching student client collects face data of a currently logged student and identifies a student identifier corresponding to the face data;
in the online teaching process, the online teaching student client acquires the head image of the current student according to a preset period, and uploads the head image of the current student and the corresponding student identification to the remote server;
the remote server carries out real-time image processing on the head images uploaded by the student client terminals to obtain the lecture attending action information of the students on line;
the remote server determines the on-class concentration degree of each online student according to the lecture attending action information of each online student;
and the remote server sorts all the online student identifications according to the class concentration of all the online students and provides the sorting result to the client of the online teaching teacher.
In an alternative embodiment, the lecture attending action information includes but is not limited to: eye closure and head pose.
In an optional embodiment, the remote server performs real-time image processing on the head image uploaded by each student client to obtain the eye closure degree of each online student, including:
the remote server detects the face in each received head image by using a preset face detection algorithm and positions the eyes in the detected face;
the remote server acquires the eyelid length and the eye width of the positioned eye; the eyelid length is the length between an upper eyelid positioning point and a lower eyelid positioning point, and the eye width is the distance between a left eye corner positioning point and a right eye corner positioning point;
the remote server calculates the eye closure value of each online student according to the following formula:
Figure BDA0003029671560000021
wherein E isiIs the eye closure degree, h, of the ith online studentiEyelid length, l, for the ith online studentiThe eye width of the ith online student is represented, and i is 1, 2.
In an optional embodiment, the performing, by the remote server, real-time image processing on the head image uploaded by each student client to obtain the head pose of each online student includes:
the remote server detects the feature points of the left corner of the left eye, the right corner of the right eye, the nose tip, the chin, the left mouth corner and the right mouth corner of each online student in the received head image through a digital library;
the remote server calculates a rotation vector corresponding to the head of each online student through a solvePnP function of the OpenCV according to the detected feature points;
and the remote server calculates the roll angle, the pitch angle and the yaw angle of the head of each online student in a three-dimensional space according to the corresponding rotation vector of the head of each online student.
In an alternative embodiment, the remote server calculates the roll angle, pitch angle and yaw angle of the head of each online student in the three-dimensional space according to the rotation vector corresponding to the head of each online student, and the method comprises the following steps:
the remote serverConstructing a rotation vector meeting a condition x according to the corresponding head of the ith online student2+y2+z2+w2Quaternion of 1
Figure BDA0003029671560000031
Figure BDA0003029671560000032
The unit vector of the rotating shaft of the rotating vector corresponding to the ith online student head is [ n ]x，ny，nz]The rotation angle of the rotation vector corresponding to the ith online student head is alpha;
the remote server calculates the roll angle, pitch angle and yaw angle of the head of the ith online student in the three-dimensional space according to the following formulas:
Figure BDA0003029671560000033
wherein,
Figure BDA0003029671560000034
θi，ψirespectively representing the roll angle, the pitch angle and the yaw angle of the head of the ith online student in a three-dimensional space.
In an optional embodiment, the remote server determines the on-class concentration degree of each online student according to the on-class action information of each online student, including:
the class concentration of each online student is calculated according to the following formula:
Figure BDA0003029671560000035
wherein: siThe concentration degree of the ith online student.
In an optional embodiment, the remote server ranks all online student identities according to the on-class concentration of all online students, including:
the remote server calculates concentration ranking values of all online students according to the on-class concentration of all the online students;
and the remote server sorts all the online student identifications according to the size of the concentration degree ranking value of each online student.
In an optional embodiment, the remote server calculates the concentration ranking value of each online student according to the class concentration of all online students, including:
the remote server calculates the concentration ranking value of each online student according to the following formula:
Figure BDA0003029671560000041
wherein D isiRanking the value of the concentration degree for the ith online student, SiConcentration degree for ith online student, Si+aConcentration of the i + a th online student, a ═ 1-i, 2-i,.., n-i; u () is a step function.
The invention provides a remote online course teaching management method, which collects head images of current students according to a preset period, uploads the head images of the current students and corresponding student identifications to a remote server, the remote server carries out real-time image processing on the head images uploaded by each student client to obtain the lecture attending action information of each online student, and determines the lecture attending concentration degree of each online student according to the lecture attending action information of each online student, thereby sequencing all online student identifications and providing the sequencing result to an online teaching teacher client. The method can realize real-time supervision of the student listening state and provide the supervision for the teacher so that the teacher can perform corresponding classroom management according to the student listening state, the method increases the automatic management function of the remote online teaching system, the management method is simple and effective, and the learning efficiency of student users can be improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a remote online course teaching management method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for the remote server to derive eye closure of an online student from a head image;
FIG. 3 is a flow chart of a method for the remote server to derive the head pose of the on-line student from the head image;
fig. 4 is a flowchart of step S105.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a remote online course teaching management method according to an embodiment of the present invention. Referring to fig. 1, the method comprises the steps of:
s101: the online teaching student client collects face data of a currently logged student and identifies a student identifier corresponding to the face data.
In the embodiment of the invention, after a student logs in a remote online teaching system through a student client, the student client acquires face image data of the student through preset camera and other camera equipment, performs face recognition on the acquired face image data through a preset face recognition algorithm, matches the recognized face data with face data corresponding to a plurality of prestored student identifications, and takes the student identification corresponding to the matched face data as the student identification of the current logged-in student. Preferably, the student identification is a unique identification such as a student name or a student number.
S102: in the online teaching process, the client-side of the online teaching student collects the head images of the current student according to a preset period, and uploads the head images of the current student and the corresponding student identification to the remote server.
In this embodiment, after the student starts to attend a class, the on-line teaching student client acquires the head image of the current student according to a predetermined period. Preferably, the student client captures an image of the student's head every ten minutes, and then uploads the student's head image and the corresponding student identification to the remote server.
S103: the remote server carries out real-time image processing on the head images uploaded by the student client terminals to obtain the lecture attending action information of the students on line.
Preferably, the lecture attending action information includes but is not limited to: eye closure and head pose.
In an alternative embodiment, as shown in fig. 2, the method for the remote server to perform real-time image processing on the head image uploaded by each student client to obtain the eye closure degree of each online student includes the following steps S201 to S203:
s201: the remote server detects the face in each received head image using a predetermined face detection algorithm and locates the eyes in the detected face.
Preferably, in this step, the AdaBoost algorithm is used to detect the face in each head image and locate the eyes in the face, during the location, a plurality of identification location points are generated, and the connection lines of these location points form the located eye edges.
S202: the remote server obtains the eyelid length and eye width of the positioned eye.
The eyelid length is the length between the upper eyelid positioning point and the lower eyelid positioning point, and the eye width is the distance between the left eye corner positioning point and the right eye corner positioning point.
S203: the remote server calculates the eye closure value of each online student according to formula (1):
Figure BDA0003029671560000061
wherein E isiIs the eye closure degree, h, of the ith online studentiEyelid length, l, for the ith online studentiThe eye width of the ith online student is represented, and i is 1, 2.
In an alternative embodiment, as shown in fig. 3, the method for the remote server to perform real-time image processing on the head image uploaded by each student client to obtain the head pose of each online student includes the following steps S301 to S302:
s301: the remote server detects feature points of a left corner, a right corner, a nose tip, a chin, a left mouth corner and a right mouth corner of a left eye and a right eye of an ith online student in a head image of the student through a digital library;
s302: the remote server calculates a rotation vector corresponding to the head of the ith online student through a solvePnP function of the OpenCV according to the detected characteristic points of the ith online student;
in this step, for the ith online student, the direction of the calculated rotation vector is the direction of the rotation axis, which is a unit vector and is denoted as n ═ nx，ny，nz]The length of the rotation vector is the rotation angle α.
S303: and the remote server calculates the roll angle, the pitch angle and the yaw angle of the head of the student in the three-dimensional space according to the rotation vector corresponding to the head of the ith online student.
In an alternative embodiment, step S303 may include steps A1-A2:
step A1, the remote server constructs a rotation vector corresponding to the head of the ith online student according to the rotation vector2+y2+z2+w2Quaternion of 1
Figure BDA0003029671560000062
Figure BDA0003029671560000063
Step A2, the remote server calculates the roll angle, pitch angle and yaw angle of the head of the ith online student in the three-dimensional space according to the following formula (2):
Figure BDA0003029671560000064
wherein,
Figure BDA0003029671560000071
θi，ψirespectively representing the roll angle, the pitch angle and the yaw angle of the head of the ith online student in a three-dimensional space.
In the embodiment, the orthogonal projection iterative transformation algorithm is used for recognizing the head postures of the students, and the head deflection condition of the students is calculated so as to judge whether the listening and speaking states of the students are centralized or not.
S104: and the remote server determines the class concentration degree of each online student according to the class attending action information of each online student.
In this embodiment, an increased class concentration assessment value is calculated according to the student attending action information, and is used for quantifying the concentration level of the student attending class.
In an alternative embodiment, the on-class concentration of each online student may be calculated according to the following equation (3):
Figure BDA0003029671560000072
wherein: siThe concentration degree of the ith online student.
S105: and the remote server sorts all the online student identifications according to the class concentration of all the online students and provides the sorting result to the client of the online teaching teacher.
In this step, according to all online students 'concentration degree in class, sort all online student's sign to giving the online teaching teacher customer end with the sequencing result, can make the teacher to student's concentration degree height in class surveyability at a glance, with master student's condition in the shortest time, carry out in good time to asking the classmates that the concentration degree is not high, reach the purpose of reminding the classmates to listen to the speech seriously, can let mr better control the classroom simultaneously, improve classroom teaching efficiency.
In an alternative embodiment, the executing subjects of the above steps S102-S104 may also be student clients, that is: in the online teaching process, after the client of the online teaching student collects the head image of the current student according to the preset period, the client of the online teaching student can locally perform real-time image processing on the head image of the local login student to obtain the lesson attending action information of the online student, then determines the lesson attending concentration degree of the online student according to the lesson attending action information of the online student, and then sends the calculated lesson attending concentration degree of the local online student to a remote server for sequencing, which is not described herein any more.
As shown in fig. 4, step S105 may include the following steps S401 to S403:
s401: the remote server calculates the concentration degree ranking value of each online student according to the class concentration degree of all online students;
preferably, the remote server calculates the concentration ranking value of each online student according to the following formula (4):
Figure BDA0003029671560000081
wherein D isiRanking the value of the concentration degree for the ith online student, SiConcentration degree for ith online student, Si+aConcentration of the i + a th online student, a ═ 1-i, 2-i,.., n-i; u () is a step function, and is 1 when the value in the parentheses is 0 or more, and is 0 when the value in the parentheses is less than 0. From this formula, the higher the concentration of the student in class, the smaller the corresponding ranking value.
S402: and the remote server sorts all the online student identifications according to the size of the concentration degree ranking value of each online student.
Preferably, the concentration ranking values of the online students are ranked in descending order, that is: the student identities with the greater concentration ranking value (corresponding to the lower class concentration) are ranked in front.
S403: and providing the sequencing result to an online teacher client for display.
In this step, if there is no student identifier in the ranking result, the ranking result may be presented in a predetermined presentation interface window according to a predetermined format, for example, student identifiers may be presented in the presentation window from top to bottom, and the concentration level of the students ranked at the top is the lowest; if the sequencing result includes student identifiers in parallel (with the same ranking value), the student identifiers with the same ranking value can be displayed in parallel in a predetermined display interface window, for example, if the student identifiers are displayed from top to bottom in the display window, the students in the uppermost row have the lowest concentration in class, and the student identifiers with the same ranking value are displayed in the same row.
The invention provides a remote online course teaching management method, which collects head images of current students according to a preset period, uploads the head images of the current students and corresponding student identifications to a remote server, the remote server carries out real-time image processing on the head images uploaded by each student client to obtain the lecture attending action information of each online student, and determines the lecture attending concentration degree of each online student according to the lecture attending action information of each online student, thereby sequencing all online student identifications and providing the sequencing result to an online teaching teacher client. The method can realize real-time supervision of the student listening state and provide the supervision for the teacher so that the teacher can perform corresponding classroom management according to the student listening state, the method increases the automatic management function of the remote online teaching system, the management method is simple and effective, and the learning efficiency of student users can be improved.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations. The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A remote online course teaching management method is characterized by comprising the following steps:
the online teaching student client collects face data of a currently logged student and identifies a student identifier corresponding to the face data;
in the online teaching process, the online teaching student client acquires the head image of the current student according to a preset period, and uploads the head image of the current student and the corresponding student identification to the remote server;
the remote server carries out real-time image processing on the head images uploaded by the student client terminals to obtain the lecture attending action information of the students on line;
the remote server determines the on-class concentration degree of each online student according to the lecture attending action information of each online student;
and the remote server sorts all the online student identifications according to the class concentration of all the online students and provides the sorting result to the client of the online teaching teacher.
2. The remote online course teaching management method as claimed in claim 1, wherein said lecture attending action information includes but is not limited to: eye closure and head pose.
3. The method as claimed in claim 2, wherein said remote server performs real-time image processing on the head images uploaded by the student clients to obtain the eye closure of each online student, and comprises:
the remote server detects the face in each received head image by using a preset face detection algorithm and positions the eyes in the detected face;
the remote server acquires the eyelid length and the eye width of the positioned eye; the eyelid length is the length between an upper eyelid positioning point and a lower eyelid positioning point, and the eye width is the distance between a left eye corner positioning point and a right eye corner positioning point;
the remote server calculates the eye closure value of each online student according to the following formula:
Figure FDA0003029671550000011
wherein E isiIs the eye closure degree, h, of the ith online studentiEyelid length, l, for the ith online studentiThe eye width of the ith online student is represented, i is 1,2, … n, and n is the total number of online students.
4. The method as claimed in claim 3, wherein said remote server performs real-time image processing on the head images uploaded by the student clients to obtain the head postures of the students, comprising:
the remote server detects the feature points of the left corner of the left eye, the right corner of the right eye, the nose tip, the chin, the left mouth corner and the right mouth corner of each online student in the received head image through a digital library;
the remote server calculates a rotation vector corresponding to the head of each online student through a solvePnP function of the OpenCV according to the detected feature points;
and the remote server calculates the roll angle, the pitch angle and the yaw angle of the head of each online student in a three-dimensional space according to the corresponding rotation vector of the head of each online student.
5. The remote online course teaching management method as claimed in claim 4, wherein said remote server calculates a roll angle, a pitch angle and a yaw angle of the head of each online student in a three-dimensional space according to the rotation vector corresponding to the head of each online student, comprising:
the remote server constructs a rotation vector corresponding to the head of the ith online student according to the rotation vector2+y2+z2+w2Quaternion of 1
Figure FDA0003029671550000021
Figure FDA0003029671550000022
The unit vector of the rotating shaft of the rotating vector corresponding to the ith online student head is [ n ]x，ny，nz]The rotation angle of the rotation vector corresponding to the ith online student head is alpha;
the remote server calculates the roll angle, pitch angle and yaw angle of the head of the ith online student in the three-dimensional space according to the following formulas:
Figure FDA0003029671550000023
wherein,
Figure FDA0003029671550000024
θi，ψirespectively representing the roll angle, the pitch angle and the yaw angle of the head of the ith online student in a three-dimensional space.
6. The method as claimed in claim 5, wherein the step of determining the on-class concentration of each online student according to the on-class action information of each online student by the remote server comprises:
the class concentration of each online student is calculated according to the following formula:
Figure FDA0003029671550000031
wherein: siThe concentration degree of the ith online student.
7. The remote online course teaching management method according to any of claims 3-6, wherein the remote server ranks all online student identities according to their on-class concentration, including:
the remote server calculates concentration ranking values of all online students according to the on-class concentration of all the online students;
and the remote server sorts all the online student identifications according to the size of the concentration degree ranking value of each online student.
8. The remote online course teaching management method as claimed in claim 7, wherein the remote server calculates concentration ranking value of each online student according to the on-class concentration of all online students, comprising:
the remote server calculates the concentration ranking value of each online student according to the following formula:
Figure FDA0003029671550000032
wherein D isiRanking the value of the concentration degree for the ith online student, SiConcentration degree for ith online student, Si+aConcentration degree of the i + a position online student, a is 1-i, 2-i, …, n-i; u () is a step function.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113783709A (en) * 2021-08-31 2021-12-10 深圳市易平方网络科技有限公司 Conference system-based participant monitoring and processing method and device and intelligent terminal
CN113783709B (en) * 2021-08-31 2024-03-19 重庆市易平方科技有限公司 Conference participant monitoring and processing method and device based on conference system and intelligent terminal
CN116665291A (en) * 2023-07-24 2023-08-29 泸州职业技术学院 Image processing system and image processing method
CN116665291B (en) * 2023-07-24 2023-10-03 泸州职业技术学院 Image processing system and image processing method

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