Multi-student individual segmentation and state autonomous identification method based on deep learning

A deep learning and student technology, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve problems such as high cost and low accuracy

Active Publication Date: 2020-08-07
NANING HANTAO TECH +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, there are few methods for identifying students' listening status. There are methods based on human body feature point recognit

Method used

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  • Multi-student individual segmentation and state autonomous identification method based on deep learning
  • Multi-student individual segmentation and state autonomous identification method based on deep learning
  • Multi-student individual segmentation and state autonomous identification method based on deep learning

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Embodiment Construction

[0090] In this embodiment, taking a lecture experiment video with 50 frames of images extracted every 10s as an example, the method for individual detection of middle school students and autonomous identification of the lecture status during the complete classroom period is described;

[0091] refer to figure 1 , is a schematic workflow diagram of the deep learning-based multi-student individual segmentation and state autonomous identification method provided by the embodiment of the present invention, including the following steps:

[0092] Step 1: Collect the normal class video, and process the collected video into frames to obtain an image to be processed every 10 seconds, and combine all the obtained images to obtain continuous frame images of the classroom video;

[0093] Step 2: Use the multi-student individual segmentation method based on Mask R-CNN to segment the student individuals and non-student individuals in the continuous frame images of the classroom video, and ...

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Abstract

The invention provides a multi-student individual segmentation and state autonomous recognition method based on deep learning, and the method comprises the steps: collecting a normal class video, carrying out the framing, and obtaining continuous frame images of a class video; segmenting out students' individuals and non-students' individuals, and marking the non-students' individuals as masks ofdifferent colors; finding a human eye feature point of each student individual through a human face feature point positioning method, calculating a human eye closure feature value of each student individual, and judging whether each student individual is in a lecture attending state at present or not; judging whether faces are detected by all the student individuals or not, and judging the class attending state grades of the student individuals; and designing the student lecture attending efficiency evaluation method in the whole class period according to whether each student is in a lecture attending state or not and whether each student is in a non-head-up state or not. The invention provides a solution for realizing the autonomous identification of the student lecture attending state and the judgment of the lecture attending efficiency, and has the advantages of high speed, high identification rate and strong environmental adaptability.

Description

technical field [0001] The invention relates to the technical field of machine learning and machine vision, in particular to a method for individual segmentation of multiple students and autonomous state recognition based on deep learning. Background technique [0002] In recent years, the concept of smart classroom has emerged. It is an emerging concept that efficiently integrates advanced information collection and transmission technology, various intelligent sensing technologies, and computer processing technology into the education field. The classroom is the core of education, and the status of students attending classes is the most direct and effective reflection of classroom efficiency. The existing classroom teaching feedback is still in the subjective manual analysis stage, which consumes energy, is inefficient, and cannot comprehensively monitor each student's performance. state. Target segmentation is the basis of visual analysis. Convolutional neural networks ar...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/165G06V40/171G06N3/045G06F18/214Y02D10/00
Inventor 李涛谢非光蔚然章悦汪璠陆飞钱伟行杨德明张炜邱爽
Owner NANING HANTAO TECH
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