CNNs and Transfer Learning for Lecture Venue Occupancy and Student Attention Monitoring
CNNs and Transfer Learning for Lecture Venue Occupancy and Student Attention Monitoring
复制标题
用于演讲场地占用和学生注意力监控的 CNN 和迁移学习
DOI：
10.1007/978-3-030-33723-0_31
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Shengzhi Du
中科院分区：
文献类型：
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作者：
Antonie J. Smith;B. J. Wyk;Shengzhi Du
在某些情况下，高等教育中学生成功率较低可能是由于学生人数空前增加，而资源和资金却没有相应增加。本文提出了一种基于人脸的检测系统，使用最先进的深度卷积神经网络（CNN）架构来监控拥挤教室中的占用率和学生注意力。拟议的系统的目的是通过监测出勤率和注意力来提高科目成功率。该系统采用两个阶段的方法：第一阶段确定图像帧中学生面孔的数量。比较了Haar Cascade、LBP、HOG、Resnet CNN、TinyFace CNN和SSD，以确定最适合在拥挤的教室场景中检测人脸的算法。在第二阶段，使用迁移学习确定面部的方向。面被分类为“右”、“左”或“中心”。该信息显示在增强现实显示器上，以半实时地向讲师提供反馈。希望这将有助于讲师解决与拥挤的教室中的学生注意力有关的问题。
Lower student success rates in higher education might, in some case, be due to the unprecedented increase of student numbers without a comparable increase in resources and funding. This paper proposes a face-based detection system to monitor occupancy and student attention in crowded classroom using state of the art deep Convolutional Neural Networks (CNN) architectures. The aim of the proposed system is to contribute to the increase of subject success rates by monitoring attendance and attention. The system utilizes a two-phased approach: The first phase determines the number of student faces in an image frame. The Haar Cascade, LBP, HOG, Resnet CNN, TinyFace CNN, and SSD were compared to determine the algorithm best suited to the detection of faces in crowded classroom scenes. In phase two, the orientations of the faces are determined using transfer learning. Faces are classified as “right”, “left”, or at the “center”. This information is displayed on an augmented reality display to provide feedback to lecturers in semi real-time. It is hoped that this will assist lecturers to address problems related to student attention in crowded classrooms.