Accurately Estimate and Analyze Human Postures in Classroom Environments
Estimating human posture in crowded smart teaching environments is a fundamental technical challenge for measuring learners’ engagement levels. This work presents a model for detecting critical points in human posture using ECAv2-HRNet in crowded situations. The paper introduces a method called ECAv...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/4/313 |
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| Summary: | Estimating human posture in crowded smart teaching environments is a fundamental technical challenge for measuring learners’ engagement levels. This work presents a model for detecting critical points in human posture using ECAv2-HRNet in crowded situations. The paper introduces a method called ECAv2Net, which combines a channel feature reinforcement method with the ECANet attention mechanism network, this innovation improves the performance of the network. Additionally, ECAv2Net is integrated into the high-resolution network HRNet to create ECAv2-HRNet. This fusion allows for the incorporation of more useful feature information without increasing the model parameters. The paper also presents a human posture dataset called GUET CLASS PICTURE, which is designed for dense scenes. The experimental results when using this dataset, as well as a public dataset, demonstrate the superior performance of the human posture estimation model based on ECAv2-HRNet proposed in this paper. |
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| ISSN: | 2078-2489 |