WO2020082971A1 - 一种课堂实时监测与评估系统及其工作方法、创建方法 - Google Patents
一种课堂实时监测与评估系统及其工作方法、创建方法 Download PDFInfo
- Publication number
- WO2020082971A1 WO2020082971A1 PCT/CN2019/107886 CN2019107886W WO2020082971A1 WO 2020082971 A1 WO2020082971 A1 WO 2020082971A1 CN 2019107886 W CN2019107886 W CN 2019107886W WO 2020082971 A1 WO2020082971 A1 WO 2020082971A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- classroom
- module
- real
- network
- time
- Prior art date
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 46
- 238000012544 monitoring process Methods 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000006399 behavior Effects 0.000 claims abstract description 33
- 230000015654 memory Effects 0.000 claims abstract description 26
- 238000010191 image analysis Methods 0.000 claims abstract description 23
- 206010000117 Abnormal behaviour Diseases 0.000 claims abstract description 18
- 238000010195 expression analysis Methods 0.000 claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 230000014509 gene expression Effects 0.000 claims description 27
- 238000011176 pooling Methods 0.000 claims description 15
- 230000003542 behavioural effect Effects 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 7
- 238000007619 statistical method Methods 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 230000008921 facial expression Effects 0.000 claims description 5
- 230000008676 import Effects 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 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
- 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
- G06Q50/205—Education administration or guidance
-
- 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
Definitions
- the invention relates to the field of wisdom education, in particular to a classroom real-time monitoring and evaluation system and its working method and creation method.
- classroom teaching is the most basic and most important part of theoretical knowledge transfer in the process of talent training, and its quality has a direct impact on the quality of talent training.
- Practice shows that the monitoring and evaluation of classroom effects has a positive role in improving the quality of classroom teaching, evaluating teachers 'classroom teaching, helping teachers understand students' learning situation, updating teaching content, adjusting teaching programs, and improving teaching methods.
- the common classroom effect evaluation is divided into on-site observation evaluation, monitoring monitoring evaluation, video evaluation, scale evaluation, etc. It is generally used to evaluate teachers; while teachers ’way of understanding students is mainly through homework or tests, lagging behind Sex is relatively large.
- the purpose of the present invention is to provide a classroom real-time detection and evaluation system, which can display the student's performance in real time, help the teacher to grasp the situation of the student at any time, promptly remind the students who are absent from the lecture, change the teaching method, adjust the teaching plan, and effectively improve the teaching quality .
- the present invention provides a real-time classroom monitoring and evaluation system.
- the real-time classroom monitoring and evaluation system is an FPGA system, which includes a video acquisition module, a data scheduling module, an image analysis module, and a result statistics module connected in sequence;
- the video collection module is used to collect video images in the classroom in real time, parse the collected video images into several frames, and then send them to the data scheduling module in sequence;
- the data scheduling module includes at least one first memory and a first-in first-out queue sub-module.
- the data scheduling module sends the received image frames to the first memory, and enters the image analysis module in turn through the first-in first-out queue to perform image analysis;
- the image analysis module includes a behavioral expression analysis network model based on the YOLO network.
- the behavioral expression analysis network model includes several network layers set in pipeline, two second memories for storing output results of each network layer, and YOLO output layer to output analysis results;
- Each of the network layers includes a convolution layer and a pooling layer that are correspondingly set, and the output results of the convolution layer and the pooling layer are alternately written into two second memories to implement a ping-pong operation;
- the start-up switching between the layers of the behavior expression analysis network model is determined by the handshake signal, that is, the convolutional layer and the pooling layer started by the handshake signal alternately read data from the two second memories, process and send To the next network layer;
- the YOLO output layer is respectively connected to the last level network layer and the result statistics module to transmit the output results to the result statistics module;
- the result statistics module receives the analysis result sent by the image analysis module, and performs statistical analysis on the abnormal behavior therein.
- the first memory is a synchronous dynamic random access memory
- the second memory is a static random access memory.
- the pooling layer is the largest pooling layer.
- an image adjustment module is provided between the data scheduling module and the image analysis module for receiving the image output by the data scheduling module, and after scaling, it is sent to two second memories for the network layer to call.
- the behavior expression analysis network model further has a weight setting module, which is used to set the weight of the convolution kernel of the network layer.
- the working method includes:
- classroom performance includes both abnormal behavior and normal behavior
- a warning is issued when the number of students responding to abnormal behavior at the same time is greater than a set number threshold.
- the method further includes:
- Classroom performance is scored on the behavioral expressions of students in each frame of images collected. Classroom performance below the set score is defined as abnormal behavior.
- the method further includes:
- ⁇ i is the weight that scores in each time period occupy when calculating the total score of teaching results
- the method further includes:
- the statistical analysis result of the result statistical module is sent to a designated client after the end of the class.
- the invention also refers to a method for creating a classroom real-time monitoring and evaluation system.
- the method for creating a classroom real-time monitoring and evaluation system includes:
- S1 Collect students' behavior and expression images, and superimpose a standard facial expression database to create a classroom student behavior expression database;
- step S3 According to the network parameters obtained in step S2, a real-time classroom monitoring and evaluation system is established.
- FIG. 1 is a schematic structural diagram of a classroom real-time monitoring and evaluation system of the present invention.
- FIG. 2 is a schematic diagram of the YOLO V3_tiny network structure of the present invention.
- FIG. 3 is an architecture diagram of FPGA design and implementation of the target detection network of the present invention.
- FIG. 4 is a schematic diagram of the working method of the classroom real-time monitoring and evaluation system of the present invention.
- FIG. 5 is a schematic diagram of a method for creating a classroom real-time monitoring and evaluation system of the present invention.
- the classroom real-time monitoring and evaluation system is an FPGA system, which includes a video acquisition module 10, a data scheduling module 20, an image analysis module 30, and result statistics connected in sequence The module 40 and a processor module (not shown in the figure) coordinating the normal operation of all modules.
- the video collection module 10 is used to collect video images in the classroom in real time, parse the collected video images into several frames, and then send them to the data scheduling module 20 in sequence.
- the data scheduling module 20 includes at least one first memory and a first-in first-out queue sub-module.
- the data scheduling module 20 sends the received image frames to the first memory, and then enters the image analysis through the first-in first-out queue.
- the module 30 performs image analysis.
- the first memory is a synchronous dynamic random access memory.
- the image analysis module 30 includes a behavioral expression analysis network model based on the YOLO network.
- the behavioral expression analysis network model includes several network layers set in pipeline, two second memories for storing output results of each network layer, and YOLO output layer for outputting analysis results.
- the second memory is a static random access memory.
- the YOLO network used here is the YOLO V3_tiny network. Compared with the commonly used V1 network and V2 network, the V3 network has the characteristics of fast response and better performance. It is suitable for classroom analysis of this crowd with high density and behavioral expression characteristics Used in more occasions.
- each of the network layers includes a convolution layer and a pooling layer that are correspondingly set, and the output results of the convolution layer and the pooling layer are alternately written into two second memories to implement a ping pong operation.
- the pooling layer is the largest pooling layer.
- the start-up switching between the layers of the behavior expression analysis network model is determined by the handshake signal, that is, the convolutional layer and the pooling layer started by the handshake signal alternately read data from the two second memories, process and send To the next network layer.
- the YOLO output layer is respectively connected to the last-level network layer and the result statistics module 40 to transmit the output results to the result statistics module 40.
- the result statistics module 40 receives the analysis result sent by the image analysis module 30 and performs statistical analysis on the abnormal behavior therein.
- an image adjustment module is provided between the data scheduling module 20 and the image analysis module 30 for receiving the image output by the data scheduling module 20, and after scaling, it is sent to two second memories for the network layer to call.
- the data adjustment module is used to scale the image frame output by the data scheduling module 20 and send it to the image analysis module 30, so that the image frame conforms to the reception standard of the image analysis module 30.
- an image frame has a pixel ratio of 1920 * 1080, and is processed by an image adjustment module to be converted into an image with a pixel ratio of 288 * 288 to enter the image analysis module 30 for specific behavior expression analysis.
- the behavioral expression analysis network model also has a weight setting module for setting the convolution kernel weight of the network layer.
- the convolution kernel weight has no fixed value and needs to be set by the user according to actual needs.
- the present invention also refers to a working method of the classroom real-time monitoring and evaluation system.
- the working method includes:
- the classroom performance includes two types of abnormal behavior and normal behavior, and the number of students responding to abnormal behavior at the same moment Once a number threshold is set, a warning is issued.
- the classroom real-time monitoring and evaluation system also includes a warning module to issue a warning to remind teachers to pay attention to abnormal behaviors in the classroom in time.
- the criteria for determining abnormal behavior can be set as follows:
- Classroom performance is scored on the behavioral expressions of students in each frame of images collected. Classroom performance below the set score is defined as abnormal behavior.
- students A ’s eyes are fixed on the teacher, they are sitting upright, they are raising their hands to answer questions, and the expression is happy, each scores one point, and student A ’s score is 4 points; student B ’s eyes are fixed on the teacher and the expression is happy, and each one Points, but the action is to lie on the table and deduct one point, and Student B scores 1 point. If the set score is 2 points, the behavior of student A is judged as normal behavior, and the behavior of student B is judged as abnormal behavior.
- the result statistics module 40 calculates the total score of a class ’s teaching results.
- the time factor can be taken into account to obtain a more objective and accurate judgment result.
- the method further includes:
- ⁇ i is the weight that scores in each time period occupy when calculating the total score of teaching results
- the method also includes:
- the statistical analysis result of the result statistical module 40 is sent to a designated client after the end of the class, such as the mobile phone of the corresponding teacher, the mobile phone of the staff responsible for evaluating the effect of the class, and so on.
- the present invention also refers to a method for creating a classroom real-time monitoring and evaluation system.
- the method for creating a classroom real-time monitoring and evaluation system includes:
- S1 Collect students' behavior and expression images, superimpose a standard facial expression database to create a classroom student behavior expression database.
- the method of creating a database of classroom student behavior expressions also includes: annotating the collected image data, not only annotating the area of the target face but also annotating its behavior and expression.
- S2 In-depth network training is performed on the data in the classroom expression database of classroom students, and the network parameters are obtained after many cycles.
- the deep network is trained by the BP algorithm, and the best network parameters are obtained through multiple cycles.
- step S3 According to the network parameters obtained in step S2, a classroom real-time monitoring and evaluation system is established.
- the classroom real-time monitoring and evaluation system is an FPGA system, and the data collected by the system does not need to be uploaded to the cloud to maximize the protection of the privacy of students and teachers.
- the classroom behavior and expression database is based on the accumulated student data and the collected images to record student reading, taking notes, listening, raising hands and lying on the table, as well as expressions of happiness, disgust, daze, surprise, anger, etc.
- classroom student expression database training network parameters.
- the real-time display of the learning status of the students in the classroom is to capture the students 'facial expressions and behaviors, statistically analyze the students' classroom performance, and remind abnormal behaviors (such as the phenomenon of a large number of students sleeping in groups and playing mobile phones).
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Primary Health Care (AREA)
- Marketing (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Social Psychology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Claims (10)
- 一种课堂实时监测与评估系统,其特征在于,所述课堂实时监测与评估系统为一FPGA系统,包括依次连接的视频采集模块、数据调度模块、图像分析模块、结果统计模块;所述视频采集模块用以实时采集教室内的视频图像,将采集的视频图像解析成若干帧后依次发送至数据调度模块;所述数据调度模块包括至少一个第一存储器和一先入先出队列子模块,数据调度模块将接收到的图像帧发送至第一存储器,经过先入先出队列依次进入图像分析模块进行图像分析;所述图像分析模块包括一基于YOLO网络的行为表情分析网络模型,该行为表情分析网络模型包括若干个流水设置的网络层、用以存储每个网络层输出结果的两个第二存储器、以及用以输出分析结果的YOLO输出层;每个所述网络层包括对应设置的卷积层与池化层,卷积层和池化层的输出结果交替写入两个第二存储器中,以实现乒乓操作;所述行为表情分析网络模型隔层之间的启动切换由握手信号决定,即:由握手信号启动的卷积层和池化层交替地从两个第二存储器中读取数据,进行处理后发送至下一级网络层;所述YOLO输出层分别与最后一级网络层、结果统计模块连接,用以将输出结果传输至结果统计模块;所述结果统计模块接收图像分析模块发送的分析结果,对其中的异常行为进行统计分析。
- 根据权利要求1所述的课堂实时监测与评估系统,其特征在于,所述第一存储器为同步动态随机存储器;所述第二存储器为静态随机存取存储器。
- 根据权利要求1所述的课堂实时监测与评估系统,其特征在于,所述池化层为最大池化层。
- 根据权利要求1所述的课堂实时监测与评估系统,其特征在于,所述数据调度模块与图像分析模块之间设置有一图像调整模块,用以接收数据调度模块输出的图像,缩放后发送至两个第二存储器中供网络层调用。
- 根据权利要求1所述的课堂实时监测与评估系统,其特征在于,所述行为表情分析网络模型还具有一权重设置模块,用以设置网络层的卷积核权重。
- 一种基于权利要求1-5中任意一项所述的课堂实时监测与评估系统的工作方法,其特征在于,所述工作方法包括:实时采集课堂上学生的行为和表情图像,将之导入课堂实时监测与评估系统进行课堂表现分析和统计,课堂表现包括异常行为和正常行为两种,以及响应于同一时刻的异常行为的学生数量大于一设定数量阈值,发出警告。
- 根据权利要求6所述的课堂实时监测与评估方法,其特征在于,所述方法还包括:对采集的每帧图像中的学生行为表情进行课堂表现评分,低于设定分值的课堂表现被定义成异常行为。
- 根据权利要求6所述的课堂实时监测与评估方法,其特征在于,所述方法还包括:将所述结果统计模块的统计分析结果在课堂结束后发送至一指定客户端。
- 一种课堂实时监测与评估系统的创建方法,其特征在于,所述课堂实时监测与评估系统的创建方法包括:S1:采集学生的行为和表情图像,叠加标准的人脸表情数据库以创建课堂学生行为表情数据库;S2:对课堂学生行为表情数据库中的数据进行深度网络训练,经多次循环以获取最佳的网络参数;S3:根据步骤S2中获取的网络参数以建立课堂实时监测与评估系统。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811242461.4 | 2018-10-24 | ||
CN201811242461.4A CN109359606A (zh) | 2018-10-24 | 2018-10-24 | 一种课堂实时监测与评估系统及其工作方法、创建方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2020082971A1 true WO2020082971A1 (zh) | 2020-04-30 |
Family
ID=65346517
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2019/107886 WO2020082971A1 (zh) | 2018-10-24 | 2019-09-25 | 一种课堂实时监测与评估系统及其工作方法、创建方法 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN109359606A (zh) |
WO (1) | WO2020082971A1 (zh) |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111538640A (zh) * | 2020-06-09 | 2020-08-14 | 陈君宁 | 一种动态解析的数据链 |
CN111931598A (zh) * | 2020-07-20 | 2020-11-13 | 湖北美和易思教育科技有限公司 | 一种基于人脸识别的课堂智能实时分析方法及系统 |
CN112116264A (zh) * | 2020-09-24 | 2020-12-22 | 北京易华录信息技术股份有限公司 | 一种活跃度评估方法及装置 |
CN113065441A (zh) * | 2021-03-25 | 2021-07-02 | 开放智能机器(上海)有限公司 | 一种基于边缘设备的图像处理系统及方法 |
CN113743250A (zh) * | 2021-08-16 | 2021-12-03 | 华中师范大学 | 一种课堂教学行为事件描述模型的构建方法及系统 |
CN115130932A (zh) * | 2022-08-31 | 2022-09-30 | 中国医学科学院阜外医院 | 一种课堂活动数字化评估方法 |
CN115810163A (zh) * | 2022-11-17 | 2023-03-17 | 云启智慧科技有限公司 | 一种基于ai课堂行为识别的教学评估方法和系统 |
CN116611022A (zh) * | 2023-04-21 | 2023-08-18 | 深圳乐行智慧产业有限公司 | 智慧校园教育大数据融合方法及平台 |
CN117557428A (zh) * | 2024-01-11 | 2024-02-13 | 深圳市华视圣电子科技有限公司 | 一种基于ai视觉的教学辅助方法及系统 |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109359606A (zh) * | 2018-10-24 | 2019-02-19 | 江苏君英天达人工智能研究院有限公司 | 一种课堂实时监测与评估系统及其工作方法、创建方法 |
CN110334610B (zh) * | 2019-06-14 | 2024-01-26 | 华中师范大学 | 一种基于计算机视觉的多维度课堂量化系统及方法 |
CN110598632B (zh) * | 2019-09-12 | 2022-09-09 | 深圳市商汤科技有限公司 | 目标对象的监测方法及装置、电子设备和存储介质 |
CN111291613B (zh) * | 2019-12-30 | 2024-04-23 | 新大陆数字技术股份有限公司 | 一种课堂表现评价方法及系统 |
CN111428686A (zh) * | 2020-04-14 | 2020-07-17 | 北京易华录信息技术股份有限公司 | 一种学生兴趣偏好评估方法、装置及系统 |
CN111898492A (zh) * | 2020-07-15 | 2020-11-06 | 西安石油大学 | 一种智能校园自习室监控管理系统 |
CN116051324A (zh) * | 2022-12-31 | 2023-05-02 | 华中师范大学 | 一种基于姿态检测的学生课堂参与状态评价方法及系统 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150044657A1 (en) * | 2013-08-07 | 2015-02-12 | Xerox Corporation | Video-based teacher assistance |
US20150092978A1 (en) * | 2013-09-27 | 2015-04-02 | Konica Minolta Laboratory U.S.A., Inc. | Method and system for recognition of abnormal behavior |
CN108073888A (zh) * | 2017-08-07 | 2018-05-25 | 中国科学院深圳先进技术研究院 | 一种教学辅助方法及采用该方法的教学辅助系统 |
CN108537117A (zh) * | 2018-03-06 | 2018-09-14 | 哈尔滨思派科技有限公司 | 一种基于深度学习的乘客检测方法和系统 |
CN108596037A (zh) * | 2018-03-27 | 2018-09-28 | 康体佳智能科技(深圳)有限公司 | 基于神经网络的人脸识别系统及识别方法 |
CN109359606A (zh) * | 2018-10-24 | 2019-02-19 | 江苏君英天达人工智能研究院有限公司 | 一种课堂实时监测与评估系统及其工作方法、创建方法 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105991973A (zh) * | 2015-02-26 | 2016-10-05 | 开利公司 | 用于用反馈自动调试智能视频系统的系统和方法 |
CN107895244A (zh) * | 2017-12-26 | 2018-04-10 | 重庆大争科技有限公司 | 课堂教学质量评估方法 |
CN108399376B (zh) * | 2018-02-07 | 2020-11-06 | 华中师范大学 | 学生课堂学习兴趣智能分析方法及系统 |
-
2018
- 2018-10-24 CN CN201811242461.4A patent/CN109359606A/zh active Pending
-
2019
- 2019-09-25 WO PCT/CN2019/107886 patent/WO2020082971A1/zh active Application Filing
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150044657A1 (en) * | 2013-08-07 | 2015-02-12 | Xerox Corporation | Video-based teacher assistance |
US20150092978A1 (en) * | 2013-09-27 | 2015-04-02 | Konica Minolta Laboratory U.S.A., Inc. | Method and system for recognition of abnormal behavior |
CN108073888A (zh) * | 2017-08-07 | 2018-05-25 | 中国科学院深圳先进技术研究院 | 一种教学辅助方法及采用该方法的教学辅助系统 |
CN108537117A (zh) * | 2018-03-06 | 2018-09-14 | 哈尔滨思派科技有限公司 | 一种基于深度学习的乘客检测方法和系统 |
CN108596037A (zh) * | 2018-03-27 | 2018-09-28 | 康体佳智能科技(深圳)有限公司 | 基于神经网络的人脸识别系统及识别方法 |
CN109359606A (zh) * | 2018-10-24 | 2019-02-19 | 江苏君英天达人工智能研究院有限公司 | 一种课堂实时监测与评估系统及其工作方法、创建方法 |
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111538640A (zh) * | 2020-06-09 | 2020-08-14 | 陈君宁 | 一种动态解析的数据链 |
CN111538640B (zh) * | 2020-06-09 | 2023-05-16 | 陈君宁 | 一种动态解析的数据链 |
CN111931598A (zh) * | 2020-07-20 | 2020-11-13 | 湖北美和易思教育科技有限公司 | 一种基于人脸识别的课堂智能实时分析方法及系统 |
CN111931598B (zh) * | 2020-07-20 | 2024-05-17 | 武汉美和易思数字科技有限公司 | 一种基于人脸识别的课堂智能实时分析方法及系统 |
CN112116264A (zh) * | 2020-09-24 | 2020-12-22 | 北京易华录信息技术股份有限公司 | 一种活跃度评估方法及装置 |
CN113065441A (zh) * | 2021-03-25 | 2021-07-02 | 开放智能机器(上海)有限公司 | 一种基于边缘设备的图像处理系统及方法 |
CN113743250B (zh) * | 2021-08-16 | 2024-02-13 | 华中师范大学 | 一种课堂教学行为事件描述模型的构建方法及系统 |
CN113743250A (zh) * | 2021-08-16 | 2021-12-03 | 华中师范大学 | 一种课堂教学行为事件描述模型的构建方法及系统 |
CN115130932A (zh) * | 2022-08-31 | 2022-09-30 | 中国医学科学院阜外医院 | 一种课堂活动数字化评估方法 |
CN115810163A (zh) * | 2022-11-17 | 2023-03-17 | 云启智慧科技有限公司 | 一种基于ai课堂行为识别的教学评估方法和系统 |
CN115810163B (zh) * | 2022-11-17 | 2023-09-05 | 云启智慧科技有限公司 | 一种基于ai课堂行为识别的教学评估方法和系统 |
CN116611022B (zh) * | 2023-04-21 | 2024-04-26 | 深圳乐行智慧产业有限公司 | 智慧校园教育大数据融合方法及平台 |
CN116611022A (zh) * | 2023-04-21 | 2023-08-18 | 深圳乐行智慧产业有限公司 | 智慧校园教育大数据融合方法及平台 |
CN117557428A (zh) * | 2024-01-11 | 2024-02-13 | 深圳市华视圣电子科技有限公司 | 一种基于ai视觉的教学辅助方法及系统 |
CN117557428B (zh) * | 2024-01-11 | 2024-05-07 | 深圳市华视圣电子科技有限公司 | 一种基于ai视觉的教学辅助方法及系统 |
Also Published As
Publication number | Publication date |
---|---|
CN109359606A (zh) | 2019-02-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2020082971A1 (zh) | 一种课堂实时监测与评估系统及其工作方法、创建方法 | |
CN110334610B (zh) | 一种基于计算机视觉的多维度课堂量化系统及方法 | |
CN207965910U (zh) | 基于人脸识别的教学管理系统 | |
WO2019028592A1 (zh) | 一种教学辅助方法及采用该方法的教学辅助系统 | |
WO2021047185A1 (zh) | 基于人脸识别的监测方法、装置、存储介质及计算机设备 | |
CN107239801A (zh) | 视频属性表示学习方法及视频文字描述自动生成方法 | |
CN108777087B (zh) | 一种基于云服务器的远程教育系统 | |
CN109345156A (zh) | 一种基于机器视觉的课堂教学质量评价系统 | |
CN108171414A (zh) | 教学质量评估系统 | |
CN106898169A (zh) | 一种中国计算机应用与国际互联网教学培训系统 | |
CN108596041A (zh) | 一种基于视频的人脸活体检测方法 | |
CN110619460A (zh) | 基于深度学习目标检测的教室课堂质量评估系统及方法 | |
CN111275345A (zh) | 一种基于深度学习的课堂信息化评价及管理的系统及方法 | |
CN109889881A (zh) | 一种教师课堂教学数据采集系统 | |
CN107169900A (zh) | 一种学生听课率检测方法 | |
CN206557851U (zh) | 一种教学听课情况采集装置 | |
CN115830392A (zh) | 基于改进的YOLOv5的学生行为识别方法 | |
CN104112131B (zh) | 一种用于人脸检测的训练样本的生成方法及装置 | |
CN108304779B (zh) | 一种学生教育管理的智能化调控方法 | |
CN106779362A (zh) | 一种数学学习调查方法及装置 | |
CN107680423A (zh) | 一种视频动态捕捉的在线课程进度系统 | |
CN107958500A (zh) | 一种用于教学实境信息实时采集的监控系统 | |
CN205814303U (zh) | 一种汉语标准失语症测评系统 | |
CN108492231A (zh) | 基于学生行为数据的课堂效果信息的监控系统 | |
CN107644557B (zh) | 一种基于眼球分析的课堂教学质量分析系统 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 19876987 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19876987 Country of ref document: EP Kind code of ref document: A1 |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19876987 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 13.01.2022) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19876987 Country of ref document: EP Kind code of ref document: A1 |