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7 March 2022 An automated object detection method for the attention of classroom and conference participants
Lixiong Leng, Ruoyu Leng, Zhanguo Ma, Yeguo Gong, Tanglin Wei
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Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672B (2022) https://doi.org/10.1117/12.2628648
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China
Abstract
In classroom teaching, teachers pay attention to each student's emotional changes and learning status to regulate teaching and learning to effectively improve the quality of teaching. However, the current classroom has problems such as teachers' lack of energy and delayed teaching feedback, which, to a certain extent, affect the improvement of teaching quality and hinder students' development. In recent years, with the rapid development and widespread application of information technology, new technologies such as image processing and artificial intelligence have brought new ideas and methods for research on improving teaching quality. We propose a face detection algorithm based on Yolov5, which detects the left and right head turn, head up or head down and facial expression by face images; classifies the head posture according to the left and right head turn and head up or head down, and then judges its concentration degree by combining with the detection classification of facial expression. Based on this algorithm, the concentration of classroom and meeting participants can be effectively evaluated based on face detection results, which improves the quality of classroom and meeting.
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Lixiong Leng, Ruoyu Leng, Zhanguo Ma, Yeguo Gong, and Tanglin Wei "An automated object detection method for the attention of classroom and conference participants", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672B (7 March 2022); https://doi.org/10.1117/12.2628648
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KEYWORDS
Facial recognition systems
Detection and tracking algorithms
Target recognition
Target detection
Image processing
Feature extraction
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