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Classroom student posture recognition based on an improved high-resolution network

Authors
  • Zhang, Yiwen1
  • Zhu, Tao1, 2
  • Ning, Huansheng3
  • Liu, Zhenyu1, 2
  • 1 University of South China, Hengyang, Hunan, 421001, China , Hengyang (China)
  • 2 Hunan Provincial Base for Scientific and Technological Innovation Cooperation for Medical Big Data, Hengyang, China , Hengyang (China)
  • 3 University of Science & Technology Beijing, Beijing, China , Beijing (China)
Type
Published Article
Journal
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer International Publishing
Publication Date
Jun 26, 2021
Volume
2021
Issue
1
Identifiers
DOI: 10.1186/s13638-021-02015-0
Source
Springer Nature
Keywords
Disciplines
  • Human-centered Computing in Cloud / Edge / Fog
License
Green

Abstract

Due to the large number of students in a typical classroom and crowded seating, most features of student posture are often obscured, making it difficult to balance the accuracy in identifying student postures with computational efficiency. To solve this issue, a novel classroom student posture recognition method is proposed. First, to recognize the poses of multiple students in the classroom, we use the you-only-look-once (YOLOv3) algorithm for object detection and retrain it to detect human objects that are hunching on a table, creating the pose estimation network. Next, to improve the accuracy of the pose estimation network, we use the squeeze-and-excitation network structure that is embedded in the residual structure of high-resolution networks (HRNet). Finally, with the improved HRNet algorithm’s outputs of key human body points, we design a pose classification algorithm based on a support vector machine, to classify human poses in the classroom. Experiments show that the improved HRNet multi-person pose estimation algorithm yields the best mean average precision performance of 73.76% on the common objects in context (COCO) validation dataset. We further test the proposed algorithm on a customer dataset collected in a classroom and achieved a high recognition rate of 90.1% and good robustness.

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