CN111339809A - Classroom behavior analysis method and device and electronic equipment - Google Patents
Classroom behavior analysis method and device and electronic equipment Download PDFInfo
- Publication number
- CN111339809A CN111339809A CN201811567090.7A CN201811567090A CN111339809A CN 111339809 A CN111339809 A CN 111339809A CN 201811567090 A CN201811567090 A CN 201811567090A CN 111339809 A CN111339809 A CN 111339809A
- Authority
- CN
- China
- Prior art keywords
- classroom
- behavior analysis
- video information
- feature recognition
- analysis model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 68
- 230000006399 behavior Effects 0.000 claims abstract description 80
- 238000000034 method Methods 0.000 claims abstract description 43
- 230000008569 process Effects 0.000 claims abstract description 20
- 230000008921 facial expression Effects 0.000 claims abstract description 14
- 238000007781 pre-processing Methods 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims abstract description 12
- 230000015654 memory Effects 0.000 claims description 27
- 238000004590 computer program Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 4
- 241001310793 Podium Species 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000001360 synchronised effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000001815 facial effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- 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
- G06V40/174—Facial expression recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Tourism & Hospitality (AREA)
- Multimedia (AREA)
- Human Computer Interaction (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Educational Administration (AREA)
- Evolutionary Computation (AREA)
- Psychiatry (AREA)
- Social Psychology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Educational Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a classroom behavior analysis method and device and electronic equipment, wherein the classroom behavior analysis method comprises the following steps: collecting video information in the classroom teaching process; preprocessing the video information to obtain a plurality of picture data; performing feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results; and inputting the classified picture data into the classroom behavior analysis model, and training the classroom behavior analysis model to obtain the trained classroom behavior analysis model. The invention can identify the learning state of students in the classroom teaching process, and is convenient for teachers and parents to know the school condition of the students in time.
Description
Technical Field
The invention relates to the technical field of education informatization, in particular to a classroom behavior analysis method and device and electronic equipment.
Background
At present, cameras installed in classrooms can be used for recording and broadcasting, supervising and recording attendance conditions of students, along with the development of education informatization technology, schools want to know classroom teaching conditions in various modes, teaching level is improved, and parents want to know the performance of students in a classroom. Therefore, the video information in the classroom teaching process is acquired by using the camera, and the practical situation of the classroom teaching is acquired by using informatization technologies such as data analysis, mode recognition and the like, so that the video information is a topic worthy of research and development.
Disclosure of Invention
In view of the above, the present invention provides a classroom behavior analysis method and apparatus, and an electronic device, which can identify the learning state of a student during classroom teaching, and facilitate teachers and parents to know the school situation of the student in time.
Based on the above purpose, the invention provides a classroom behavior analysis method, which comprises the following steps:
collecting video information in the classroom teaching process;
preprocessing the video information to obtain a plurality of picture data;
performing feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results;
and inputting the classified picture data into the classroom behavior analysis model, and training the classroom behavior analysis model to obtain the trained classroom behavior analysis model.
Optionally, the method further includes:
and inputting the video information into the trained classroom behavior analysis model for classroom behavior analysis.
Optionally, the video information of all students in the classroom teaching process is collected by using at least two 3D cameras installed at the positions of the podium in the classroom.
Optionally, the preprocessing includes decoding the video information by using a decoder, and obtaining the plurality of picture data after segmenting the decoded video information by using a picture segmenter.
An embodiment of the present invention further provides a classroom behavior analysis apparatus, including:
the video acquisition module is used for acquiring video information in the classroom teaching process;
the data processing module is used for preprocessing the video information to obtain a plurality of image data;
the feature recognition module is used for carrying out feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results;
and the model training module is used for inputting the classified picture data into the classroom behavior analysis model and training the classroom behavior analysis model to obtain the trained classroom behavior analysis model.
Optionally, the apparatus further comprises:
classroom behavior analysis model: and the video information analysis module is used for carrying out behavior analysis on the video information to obtain a classroom behavior analysis result.
Optionally, the video acquisition module is a 3D camera, and acquires video information of all students included in the classroom teaching process by using at least two 3D cameras installed at the positions of the classroom lecture platforms.
Optionally, the data processing module decodes the video information by using a decoder, and segments the decoded video information by using a picture splitter to obtain the plurality of picture data.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method when executing the computer program.
As can be seen from the above, the classroom behavior analysis method and device, and the electronic device provided by the invention can be used for preprocessing video information by acquiring the video information in the classroom teaching process to obtain a plurality of image data; carrying out feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results; and inputting the classified picture data into a classroom behavior analysis model to train the model to obtain a trained classroom behavior analysis model, and carrying out classroom behavior analysis on the acquired video information by using the trained classroom behavior analysis model to obtain an analysis result. The invention can identify the learning state of students in the classroom teaching process, and is convenient for teachers and parents to know the school condition of the students in time.
Drawings
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 schematic flow chart of a method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention. As shown in the figure, the classroom behavior analysis method provided by the embodiment of the present invention includes:
s10: collecting video information in the classroom teaching process;
the method comprises the steps of collecting video information of all students in a classroom teaching process by utilizing at least two 3D cameras arranged at the positions of the podium of a classroom.
S11: preprocessing video information to obtain a plurality of picture data;
the method comprises the steps of preprocessing collected video information, decoding the video information by using a decoder, and segmenting the decoded video information by using a picture segmenter to obtain a plurality of picture data.
S12: carrying out feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results;
and performing feature recognition according to facial expression features and behavior features based on the plurality of picture data. For facial expression feature recognition, facial feature information including eye feature information (inner eye corner points, outer eye corner points, iris centers and the like), mouth feature information (mouth corner points and the like) and head feature information (head vertexes and the like) is extracted in advance, picture data are recognized and processed, the picture data are compared with the facial feature information, facial expression features of students in the picture data are recognized, and the facial expression features include visual podium listening and speaking, question answering and the like. For behavior feature recognition, behavior feature information including four limbs, head positions, body positions and the like is extracted in advance, picture data are recognized and processed, the picture data are compared with the behavior feature information, and behavior features of students in the picture data are recognized, including hand raising actions, writing actions, standing actions and the like.
And after the feature recognition is finished according to the facial expression features and the behavior features, obtaining recognition results, and classifying the pictures according to the recognition results. For example, image data of a carefully listened to speech is classified into one category, image data of a hand-held person is classified into one category, image data of a written word is classified into one category, image data of a standing answer question is classified into one category, and the like.
S13: inputting the classified picture data into a classroom behavior analysis model, and training the classroom behavior analysis model to obtain a trained classroom behavior analysis model;
the classroom behavior analysis model is a mathematical model based on a deep learning algorithm, classified picture data are input into the classroom behavior analysis model for learning, and a learning result is output; and screening the learning result according to the obtained learning result, adjusting the model, and training the model again to improve the recognition rate of the model. Therefore, the classroom behavior analysis model with high recognition rate is obtained through the learning, classifying and repeated training processes of a plurality of image data.
And subsequently, performing behavior analysis on the acquired video information by using the trained classroom behavior analysis model to obtain a classroom behavior analysis result.
Fig. 2 is a schematic structural diagram of an apparatus according to an embodiment of the present invention. As shown in the drawings, the classroom behavior analysis apparatus provided in the embodiment of the present invention includes:
the video acquisition module is used for acquiring video information in the classroom teaching process; optionally, the video acquisition module is a 3D camera, and the 3D camera is used to acquire video information of all students in the classroom teaching process.
The data processing module is used for preprocessing the video information to obtain a plurality of image data; optionally, the preprocessing includes decoding the video information by using a decoder, and obtaining a plurality of picture data after segmenting the decoded video information by using a picture segmenter.
The characteristic identification module is used for carrying out characteristic identification on the plurality of image data according to facial expression characteristics and behavior characteristics to obtain characteristic identification results, and classifying the plurality of image data according to the characteristic identification results;
optionally, according to the facial expression feature recognition result and the behavior feature recognition result, the image data of the seriously attending class, the image data of the holding hand, the image data of the standing answer question, the image data of the writing, and the like are recognized.
The model training module is used for inputting the classified picture data into the classroom behavior analysis model and training the classroom behavior analysis model to obtain a trained classroom behavior analysis model;
classroom behavior analysis model: and the method is used for performing behavior analysis on the collected video information to obtain a classroom behavior analysis result.
Based on the above purpose, the embodiment of the present invention further provides an embodiment of an apparatus for a classroom behavior analysis method. The device comprises:
one or more processors, and a memory.
The apparatus for performing the classroom behavior analysis method may further include: an input device and an output device.
The processor, memory, input device, and output device may be connected by a bus or other means.
The memory, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules (e.g., the data processing module shown in fig. 2) corresponding to the classroom behavior analysis method in the embodiments of the present invention. The processor executes various functional applications and data processing of the server by running nonvolatile software programs, instructions and modules stored in the memory, so as to implement the classroom behavior analysis method of the above method embodiment.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an apparatus that performs the classroom behavior analysis method, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory optionally includes memory remotely located from the processor, and these remote memories may be connected to the member user behavior monitoring device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means may receive input numeric or character information and generate key signal inputs related to user settings and function control of the apparatus performing the classroom behavior analysis method. The output device may include a display device such as a display screen.
The one or more modules are stored in the memory and, when executed by the one or more processors, perform the classroom behavior analysis method of any of the above method embodiments. The technical effect of the embodiment of the device for executing the classroom behavior analysis method is the same as or similar to that of any method embodiment.
The embodiment of the invention also provides a non-transitory computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the processing method of the list item operation in any method embodiment. Embodiments of the non-transitory computer storage medium may be the same or similar in technical effect to any of the method embodiments described above.
Finally, it should be noted that, as will be understood by those skilled in the art, all or part of the processes in the methods of the above embodiments may be implemented by a computer program that can be stored in a computer-readable storage medium and that, when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like. The technical effect of the embodiment of the computer program is the same as or similar to that of any of the method embodiments described above.
Furthermore, the apparatuses, devices, etc. described in the present disclosure may be various electronic terminal devices, such as a mobile phone, a Personal Digital Assistant (PDA), a tablet computer (PAD), a smart television, etc., and may also be large terminal devices, such as a server, etc., and therefore the scope of protection of the present disclosure should not be limited to a specific type of apparatus, device. The client disclosed by the present disclosure may be applied to any one of the above electronic terminal devices in the form of electronic hardware, computer software, or a combination of both.
Furthermore, the method according to the present disclosure may also be implemented as a computer program executed by a CPU, which may be stored in a computer-readable storage medium. The computer program, when executed by the CPU, performs the above-described functions defined in the method of the present disclosure.
Further, it should be appreciated that the computer-readable storage media (e.g., memory) described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example, and not limitation, nonvolatile memory can include Read Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which can act as external cache memory. By way of example and not limitation, RAM is available in a variety of forms such as synchronous RAM (DRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to comprise, without being limited to, these and other suitable types of memory.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. A classroom behavior analysis method is characterized by comprising the following steps:
collecting video information in the classroom teaching process;
preprocessing the video information to obtain a plurality of picture data;
performing feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results;
and inputting the classified picture data into the classroom behavior analysis model, and training the classroom behavior analysis model to obtain the trained classroom behavior analysis model.
2. The method of claim 1, further comprising:
and inputting the video information into the trained classroom behavior analysis model for classroom behavior analysis.
3. The method as claimed in claim 1, wherein the video information of all students involved in the classroom teaching process is collected using at least two 3D cameras installed at the location of a classroom platform.
4. The method of claim 1, wherein the pre-processing comprises decoding the video information by a decoder, and segmenting the decoded video information by a picture segmenter to obtain the plurality of picture data.
5. A classroom behavior analysis apparatus, comprising:
the video acquisition module is used for acquiring video information in the classroom teaching process;
the data processing module is used for preprocessing the video information to obtain a plurality of image data;
the feature recognition module is used for carrying out feature recognition on the plurality of image data according to facial expression features and behavior features to obtain feature recognition results, and classifying the plurality of image data according to the feature recognition results;
and the model training module is used for inputting the classified picture data into the classroom behavior analysis model and training the classroom behavior analysis model to obtain the trained classroom behavior analysis model.
6. The apparatus of claim 5, further comprising:
classroom behavior analysis model: and the video information analysis module is used for carrying out behavior analysis on the video information to obtain a classroom behavior analysis result.
7. The apparatus as claimed in claim 5, wherein the video capturing module is a 3D camera, and captures the video information of all students included in the classroom teaching process using at least two 3D cameras installed at the location of the classroom platform.
8. The apparatus of claim 5, wherein the data processing module decodes the video information by a decoder, and the picture divider divides the decoded video information to obtain the plurality of picture data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 4 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811567090.7A CN111339809A (en) | 2018-12-20 | 2018-12-20 | Classroom behavior analysis method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811567090.7A CN111339809A (en) | 2018-12-20 | 2018-12-20 | Classroom behavior analysis method and device and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111339809A true CN111339809A (en) | 2020-06-26 |
Family
ID=71185734
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811567090.7A Pending CN111339809A (en) | 2018-12-20 | 2018-12-20 | Classroom behavior analysis method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111339809A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657152A (en) * | 2021-07-07 | 2021-11-16 | 国网江苏省电力有限公司电力科学研究院 | Classroom student behavior recognition system construction method |
| CN113989608A (en) * | 2021-12-01 | 2022-01-28 | 西安电子科技大学 | Student experiment classroom behavior identification method based on top vision |
| CN114708657A (en) * | 2022-03-30 | 2022-07-05 | 深圳可视科技有限公司 | Student attention detection method and system based on multimedia teaching |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105590281A (en) * | 2015-12-29 | 2016-05-18 | 南京邮电大学 | System and method for education big data processing |
| CN105653037A (en) * | 2015-12-31 | 2016-06-08 | 张小花 | Interactive system and method based on behavior analysis |
| CN106851216A (en) * | 2017-03-10 | 2017-06-13 | 山东师范大学 | A kind of classroom behavior monitoring system and method based on face and speech recognition |
| CN107133611A (en) * | 2017-06-06 | 2017-09-05 | 南京信息工程大学 | A kind of classroom student nod rate identification with statistical method and device |
| CN107316261A (en) * | 2017-07-10 | 2017-11-03 | 湖北科技学院 | A kind of Evaluation System for Teaching Quality based on human face analysis |
| CN107609517A (en) * | 2017-09-15 | 2018-01-19 | 华中科技大学 | A kind of classroom behavior detecting system based on computer vision |
| CN107644218A (en) * | 2017-09-29 | 2018-01-30 | 重庆市智权之路科技有限公司 | The method of work of crowded region behavioural analysis judgement is realized based on image collecting function |
| CN108073888A (en) * | 2017-08-07 | 2018-05-25 | 中国科学院深圳先进技术研究院 | A kind of teaching auxiliary and the teaching auxiliary system using this method |
| CN108664932A (en) * | 2017-05-12 | 2018-10-16 | 华中师范大学 | A kind of Latent abilities state identification method based on Multi-source Information Fusion |
| CN109035089A (en) * | 2018-07-25 | 2018-12-18 | 重庆科技学院 | A kind of Online class atmosphere assessment system and method |
| CN109461104A (en) * | 2018-10-22 | 2019-03-12 | 杭州闪宝科技有限公司 | Classroom monitoring method, device and electronic equipment |
-
2018
- 2018-12-20 CN CN201811567090.7A patent/CN111339809A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105590281A (en) * | 2015-12-29 | 2016-05-18 | 南京邮电大学 | System and method for education big data processing |
| CN105653037A (en) * | 2015-12-31 | 2016-06-08 | 张小花 | Interactive system and method based on behavior analysis |
| CN106851216A (en) * | 2017-03-10 | 2017-06-13 | 山东师范大学 | A kind of classroom behavior monitoring system and method based on face and speech recognition |
| CN108664932A (en) * | 2017-05-12 | 2018-10-16 | 华中师范大学 | A kind of Latent abilities state identification method based on Multi-source Information Fusion |
| CN107133611A (en) * | 2017-06-06 | 2017-09-05 | 南京信息工程大学 | A kind of classroom student nod rate identification with statistical method and device |
| CN107316261A (en) * | 2017-07-10 | 2017-11-03 | 湖北科技学院 | A kind of Evaluation System for Teaching Quality based on human face analysis |
| CN108073888A (en) * | 2017-08-07 | 2018-05-25 | 中国科学院深圳先进技术研究院 | A kind of teaching auxiliary and the teaching auxiliary system using this method |
| CN107609517A (en) * | 2017-09-15 | 2018-01-19 | 华中科技大学 | A kind of classroom behavior detecting system based on computer vision |
| CN107644218A (en) * | 2017-09-29 | 2018-01-30 | 重庆市智权之路科技有限公司 | The method of work of crowded region behavioural analysis judgement is realized based on image collecting function |
| CN109035089A (en) * | 2018-07-25 | 2018-12-18 | 重庆科技学院 | A kind of Online class atmosphere assessment system and method |
| CN109461104A (en) * | 2018-10-22 | 2019-03-12 | 杭州闪宝科技有限公司 | Classroom monitoring method, device and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 廖鹏等: "基于深度学习的学生课堂异常行为检测与分析系统", 电子世界, vol. 542, no. 08, pages 97 - 98 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657152A (en) * | 2021-07-07 | 2021-11-16 | 国网江苏省电力有限公司电力科学研究院 | Classroom student behavior recognition system construction method |
| CN113989608A (en) * | 2021-12-01 | 2022-01-28 | 西安电子科技大学 | Student experiment classroom behavior identification method based on top vision |
| CN114708657A (en) * | 2022-03-30 | 2022-07-05 | 深圳可视科技有限公司 | Student attention detection method and system based on multimedia teaching |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2020237855A1 (en) | Sound separation method and apparatus, and computer readable storage medium | |
| US20230103340A1 (en) | Information generating method and apparatus, device, storage medium, and program product | |
| US11270099B2 (en) | Method and apparatus for generating facial feature | |
| CN109766759A (en) | Emotion identification method and Related product | |
| CN106897372B (en) | Voice query method and device | |
| CN111339809A (en) | Classroom behavior analysis method and device and electronic equipment | |
| US10970909B2 (en) | Method and apparatus for eye movement synthesis | |
| CN112148922A (en) | Conference recording method, conference recording device, data processing device and readable storage medium | |
| KR102292775B1 (en) | System and method for providing learning service | |
| CN108491808B (en) | Method and device for acquiring information | |
| CN111353366A (en) | Emotion detection method and device and electronic equipment | |
| CN111353363A (en) | Teaching effect detection method and device and electronic equipment | |
| US11189183B2 (en) | Intelligent voice interaction method, device and computer readable storage medium | |
| CN111382655A (en) | Hand-lifting behavior identification method and device and electronic equipment | |
| CN113315979A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN111353364A (en) | Dynamic face identification method and device and electronic equipment | |
| Shishir et al. | Eye Assistant: Using mobile application to help the visually impaired | |
| CN111507149B (en) | Interaction method, device and equipment based on expression recognition | |
| CN110895661A (en) | Behavior identification method, device and equipment | |
| CN113657509B (en) | Teaching training lifting method, device, terminal and storage medium | |
| CN111325082A (en) | Personnel concentration degree analysis method and device | |
| CN112330579B (en) | Video background replacement method, device, computer equipment and computer readable medium | |
| CN111354377B (en) | Method and device for recognizing emotion through voice and electronic equipment | |
| CN111062232A (en) | Course information identification method and device and electronic equipment | |
| CN111914068A (en) | Method for extracting knowledge points of test questions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |