CN112381039A - Method for analyzing student learning concentration degree through AI video - Google Patents
Method for analyzing student learning concentration degree through AI video Download PDFInfo
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- CN112381039A CN112381039A CN202011350877.5A CN202011350877A CN112381039A CN 112381039 A CN112381039 A CN 112381039A CN 202011350877 A CN202011350877 A CN 202011350877A CN 112381039 A CN112381039 A CN 112381039A
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- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000013528 artificial neural network Methods 0.000 claims abstract description 29
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 230000006698 induction Effects 0.000 claims abstract description 5
- 238000007477 logistic regression Methods 0.000 claims abstract description 5
- 238000010223 real-time analysis Methods 0.000 claims abstract description 5
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 abstract description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- 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
Abstract
The invention discloses a method for analyzing the learning concentration degree of a student by an AI video, which relates to the technical field of AI video analysis, in particular to a method for analyzing the learning concentration degree of the student by the AI video, comprising the following steps: s1, collecting video data; s2, analyzing data; and S3, calculating the concentration degree. The method for analyzing the student learning concentration degree through the AI video comprises the steps of collecting video stream data, achieving based on the cooperation of two neural networks, conducting classification and induction through a Logistic regression algorithm based on KNN neural network real-time analysis, obtaining student limb action and head action vector data, taking the action vector data as an input end based on a NEAT neural network, taking a concentration degree index as an output end, achieving periodic self-learning through a typical sample marking mode, self-optimizing neural network nodes, continuously improving the concentration degree judgment accuracy, and achieving the student concentration degree judgment method.
Description
Technical Field
The invention relates to the technical field of AI video analysis, in particular to a method for analyzing the learning concentration degree of a student by an AI video.
Background
AI is an artificial intelligence technique, which is a new technical science for studying and developing theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. Image recognition technology is an important field of artificial intelligence. In order to create a computer program that simulates human image recognition activities, different image recognition models have been proposed. Such as a template matching model. Through pattern recognition and self-learning, the video content target object can be identified, and the target object is subjected to motion vector analysis. Learning-only perception of the real world is achieved.
The degree of being absorbed in student's study at present receives external disturbance easily, leads to learning inefficiency to and the unable real-time supervision student study is absorbed in the shortcoming of degree.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for analyzing the learning concentration degree of a student through an AI video, which solves the problems that the learning concentration degree of the student is easily interfered by the outside world, the learning efficiency is low and the learning concentration degree of the student cannot be monitored in real time.
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for analyzing the learning concentration degree of a student through AI videos comprises the following steps:
s1, collecting video data: video stream data acquisition is realized based on the cooperation of two neural networks;
s2, analysis data: carrying out classification induction through a Logistic regression algorithm based on KNN neural network real-time analysis to obtain student limb action and head action vector data;
s3, calculating concentration degree: and then the action vector data is used as an input end based on the NEAT neural network, and the concentration degree index is used as an output end.
Optionally, in step S1, in the step of collecting the video data, the two neural networks are a KNN neural network and a NEAT neural network, respectively.
Optionally, the step S1 of acquiring video data includes the following steps:
s101, assembling equipment: a CCD camera device is placed in front of a user desktop;
s102, video data acquisition: based on two kinds of neural networks, collecting video signals through a CCD camera, and collecting light source signals through an image sensor of a sensing system;
s103, transmitting video data: converting the light source signals in the step S102 and data acquisition into electric signals, completing the conversion of video data acquisition and transmitting the converted electric signals;
s104, storing video data: storing the video data in the step S101, the transmission video data and backing up the video data;
s105, extracting video data: and S103, extracting backup data in the video data, and providing preprocessing for video data analysis.
Optionally, the CCD camera is used for video shooting and collecting video data.
Optionally, the photoelectric conversion function is used for conversion of a photoelectric signal.
The invention provides a method for analyzing the learning concentration degree of a student by an AI video, which has the following beneficial effects:
the method for analyzing the student learning concentration degree through the AI video comprises the steps of collecting video stream data, achieving based on the cooperation of two neural networks, conducting classification and induction through a Logistic regression algorithm based on KNN neural network real-time analysis, obtaining student limb action and head action vector data, taking the action vector data as an input end based on a NEAT neural network, taking a concentration degree index as an output end, achieving periodic self-learning through a typical sample marking mode, self-optimizing neural network nodes, continuously improving the concentration degree judgment accuracy, and achieving the student concentration degree judgment method.
Detailed Description
In the following, technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
The invention provides a technical scheme that: a method for analyzing the learning concentration degree of a student through AI videos comprises the following steps:
s1, collecting video data: video stream data acquisition is realized based on the cooperation of two neural networks;
s2, analysis data: carrying out classification induction through a Logistic regression algorithm based on KNN neural network real-time analysis to obtain student limb action and head action vector data;
s3, calculating concentration degree: and then the action vector data is used as an input end based on the NEAT neural network, and the concentration degree index is used as an output end.
And step S1, in the video data acquisition, the two neural networks are a KNN neural network and a NEAT neural network respectively.
S1, the video data acquisition method comprises the following steps:
s101, assembling equipment: a CCD camera device is placed in front of a user desktop;
s102, video data acquisition: based on two kinds of neural networks, collecting video signals through a CCD camera, and collecting light source signals through an image sensor of a sensing system;
s103, transmitting video data: converting the light source signals in the step S102 and data acquisition into electric signals, completing the conversion of video data acquisition and transmitting the converted electric signals;
s104, storing video data: storing the video data in the step S101, the transmission video data and backing up the video data;
s105, extracting video data: and S103, extracting backup data in the video data, and providing preprocessing for video data analysis.
The CCD camera is used for video shooting and collecting video data.
The photoelectric conversion function is used for converting a photoelectric signal.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A method for analyzing the learning concentration degree of a student through an AI video is characterized by comprising the following steps:
s1, collecting video data: video stream data acquisition is realized based on the cooperation of two neural networks;
s2, analysis data: carrying out classification induction through a Logistic regression algorithm based on KNN neural network real-time analysis to obtain student limb action and head action vector data;
s3, calculating concentration degree: and then the action vector data is used as an input end based on the NEAT neural network, and the concentration degree index is used as an output end.
2. The AI video analysis method for student concentration in learning according to claim 1, wherein: in the step S1, in the video data, the two neural networks are a KNN neural network and a NEAT neural network, respectively.
3. The AI video analysis method for student concentration in learning according to claim 1, wherein the step of S1, capturing video data comprises the steps of:
s101, assembling equipment: a CCD camera device is placed in front of a user desktop;
s102, video data acquisition: based on two kinds of neural networks, collecting video signals through a CCD camera, and collecting light source signals through an image sensor of a sensing system;
s103, transmitting video data: converting the light source signals in the step S102 and data acquisition into electric signals, completing the conversion of video data acquisition and transmitting the converted electric signals;
s104, storing video data: storing the video data in the step S101, the transmission video data and backing up the video data;
s105, extracting video data: and S103, extracting backup data in the video data, and providing preprocessing for video data analysis.
4. The AI video analysis method of student attention to claim 3, wherein: the CCD camera is used for video shooting and collecting video data.
5. The AI video analysis method of student attention to claim 3, wherein: the photoelectric conversion function is used for conversion of photoelectric signals.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011350877.5A CN112381039A (en) | 2020-11-26 | 2020-11-26 | Method for analyzing student learning concentration degree through AI video |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011350877.5A CN112381039A (en) | 2020-11-26 | 2020-11-26 | Method for analyzing student learning concentration degree through AI video |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107918755A (en) * | 2017-03-29 | 2018-04-17 | 广州思涵信息科技有限公司 | A kind of real-time focus analysis method and system based on face recognition technology |
| CN109740446A (en) * | 2018-12-14 | 2019-05-10 | 深圳壹账通智能科技有限公司 | Classroom students ' behavior analysis method and device |
| CN111666915A (en) * | 2020-06-18 | 2020-09-15 | 上海眼控科技股份有限公司 | Monitoring method, device, equipment and storage medium |
-
2020
- 2020-11-26 CN CN202011350877.5A patent/CN112381039A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107918755A (en) * | 2017-03-29 | 2018-04-17 | 广州思涵信息科技有限公司 | A kind of real-time focus analysis method and system based on face recognition technology |
| CN109740446A (en) * | 2018-12-14 | 2019-05-10 | 深圳壹账通智能科技有限公司 | Classroom students ' behavior analysis method and device |
| CN111666915A (en) * | 2020-06-18 | 2020-09-15 | 上海眼控科技股份有限公司 | Monitoring method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
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| DARIOFLOREANO，等，程国建等译: "《仿生人工智能》", 28 February 2017, 国防工业出版社 * |
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