[1]姚 远,徐晶晶,朱小倩.基于 2D 和 3D 图像处理技术的在线学习评估[J].计算机技术与发展,2021,31(12):128-134.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 022]
　YAO Yuan,XU Jing-jing,ZHU Xiao-qian.Online Learning Evaluation Based on Processing Technologies in 2D and 3D Images[J].,2021,31(12):128-134.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 022]
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基于 2D 和 3D 图像处理技术的在线学习评估()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年12期
页码:
128-134
栏目:
应用前沿与综合
出版日期:
2021-12-10

文章信息/Info

Title:
Online Learning Evaluation Based on Processing Technologies in 2D and 3D Images
文章编号:
1673-629X(2021)12-0128-07
作者:
姚 远; 徐晶晶; 朱小倩
华中师范大学 物理科学与技术学院,湖北 武汉 430079
Author(s):
YAO Yuan; XU Jing-jing; ZHU Xiao-qian
School of Physical Science and Technology,Central China Normal University,Wuhan 430079,China
关键词:
图像处理; 在线学习; 身份识别; 姿态评估; 疲劳预警
Keywords:
image processing; online learning; identity recognition; posture assessment; fatigue warning
分类号:
TP317. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 12. 022
摘要:
新冠疫情导致全球在线教育异军突起,如何打破时间、空间的限制实时监督学生的在线学习状态随时调整教学策略进而提高学习效率具有重要意义。 基于 2D 与 3D 图像处理技术,提出了一种在线评估学习状态的方法,将学习者的学习状态分为无人、多人、用户未授权、分心以及疲劳五种。 利用 AdaBoost 算法与 ResNet 模型实现人脸检测和识别,并加入质心跟踪算法提高人脸识别检测效率;利用 RGB-D 图像实时获取人脸三维模型,通过 EPNP 算法获取学习者头部姿态进而评估学习姿态;提取学习者眼睛和嘴巴的实时图像特征,获取学习者眼睛与嘴巴纵横比值,实现学习者学习疲劳状态的实时预警。 测试表明学习者的身份识别、姿态评估以及疲劳预警有效可行,为提高在线学习的质量提供了积极思路。
Abstract:
In light of the emerging global online education resulted from the outbreak of COVID-19,how to break the time and space limits to supervise students’ online learning conditions on timely units and adjust teaching strategies freely to facilitate their learning efficiency is of paramount significance. Based on the processing technologies in 2D and 3D images,we put forward an online evaluation method of learning conditions, which divides the learning condition of learners into five categories:No Person,More than One Person,Failed Authorization from the User, Distraction and Fatigue. AdaBoost algorithm and ResNet model are used to realize face detection and recognition,and centroid tracking algorithm is added to improve the efficiency of face recognition detection. RGB - D image can be adopted to acquire 3D face models at any time while EPNP algorithm can help collect their head gesture and evaluate their learning conditions. Real-time image characteristics about their eyes and mouth is extracted to get an aspect ratio between the two parts,making it possible to achieve infinite warning of their fatigue in learning. As suggested in the test,the practice proves feasible in recognizing the learner’ s identity,evaluating his gestures and warning of their fatigue,which provides a positive thought on improving the quality of online learning.

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更新日期/Last Update: 2021-12-10