Person Detection in Collaborative Group Learning Environments Using Multiple Representations
Person Detection in Collaborative Group Learning Environments Using Multiple Representations
复制标题
DOI：
10.1109/ieeeconf53345.2021.9723388
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Wenjing Shi;M. Pattichis;Sylvia Celedón-Pattichis;Carlos López Leiva
中科院分区：
文献类型：
--
作者：
Wenjing Shi;M. Pattichis;Sylvia Celedón-Pattichis;Carlos López Leiva
我们介绍了从教室视频中检测一组学生的问题。该问题需要从不同角度对学生进行检测，并在长视频（1到1个半小时）中将该群体与其他群体分离。我们使用多种图像表示来解决该问题。我们使用FM组件将每个组与背景组分开，AM-FM组件用于检测后脑勺，YOLO用于人脸检测。我们使用来自四个不同小组的课堂视频来验证我们的方法。我们使用的多重表示被证明比单独使用YOLO更准确。
We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours).We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone.