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Classroom student behavior detection method

A detection method and student's technology, applied in neural learning methods, biometric recognition, biological neural network models, etc., can solve the problems of poor robustness, low accuracy, slow speed, etc. small image effect

Inactive Publication Date: 2020-06-30
YULIN NORMAL UNIVERSITY
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  • Summary
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AI Technical Summary

Problems solved by technology

At present, the behavior of students in the classroom is mainly detected through the full-image detection method. The full-image detection has the disadvantages of low accuracy, slow speed, and poor robustness.

Method used

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  • Classroom student behavior detection method

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the specific embodiments in the accompanying drawings.

[0029] refer to Figure 1-3 , a classroom student behavior detection method, the method comprises:

[0030] Convert the original image of the classroom students to a grayscale image;

[0031] Input the grayscale image into the deep learning model to detect the head and shoulders frame and identify the lying table, and obtain the head and shoulders frame;

[0032] Determine the hand-raising detection area according to the head-and-shoulder frame, and perform hand-raising detection in the hand-raising detection area.

[0033] According to the formula:

[0034] f(i,j)=0.2999R+0.587G+0.114B,

[0035] Perform grayscale processing on the original image to obtain a grayscale image, where f(i,j) is the grayscale value of the pixel at the image coordinates (i,j) after grayscale, and R, G, and B are the color image RGB respectively Three-componen...

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Abstract

The invention discloses a classroom student behavior detection method, relates to image processing and recognition, and mainly solves the technical problems of low accuracy, low speed and poor robustness of a current full-image detection method. The classroom student behavior detection method comprises the following steps: converting an original image of a classroom student into a grey-scale map;inputting the grey-scale map into a deep learning model to carry out head and shoulder frame detection and prone table identification, and obtaining a head and shoulder frame; determining a hand raising detection area according to the head and shoulder frame, and performing hand raising detection in the hand raising detection area. The hand raising detection area is determined according to the head and shoulder frame, and hand raising detection is performed in the hand raising detection area so that the detection input image can be greatly reduced, the detection area can be greatly reduced, the hand detection efficiency can be effectively enhanced, the hand detection accuracy can also be greatly enhanced and the robustness is high.

Description

technical field [0001] The invention relates to image processing and recognition, more specifically, it relates to a method for detecting behavior of classroom students. Background technique [0002] Image processing and recognition have been applied to all walks of life. For example, using image processing and recognition technology to detect student behavior in the classroom can help to understand and record student dynamics in a timely manner, which is conducive to strengthening classrooms and improving teaching quality. At present, the behavior of students in the classroom is mainly detected through the full-image detection method. The full-image detection has the disadvantages of low accuracy, slow speed, and poor robustness. Contents of the invention [0003] The technical problem to be solved by the present invention is aimed at the above-mentioned deficiencies of the prior art. The purpose of the present invention is to provide a classroom student behavior detectio...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06Q50/20
CPCG06N3/08G06Q50/205G06V40/107G06V40/10G06V40/117G06N3/045G06F18/241G06F18/2415
Inventor 黄艳虎巫钊王强甘国妹苏雪陈超村陈思谕王德民
Owner YULIN NORMAL UNIVERSITY
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