基于改进YOWO算法的学生课堂行为识别
Classroom Behavior Recognition of Students Based on Improved YOWO Algorithm
徐鑫磊 1张景异1
作者信息
- 1. 沈阳理工大学自动化与电气工程学院,沈阳 110159
- 折叠
摘要
当前,大部分的学生课堂行为识别工作主要基于单帧图像进行,忽略了行为的连贯性,因此不能充分利用视频信息来对学生的课堂行为进行准确刻画.所以,本文提出一种改进的YOWO算法模型,有效利用视频信息对学生课堂行为进行识别.首先,本文采集某高校真实课堂教学中的授课录像,制作出包含 5 类学生课堂行为的AVA格式视频数据集;其次,采用时移模块TSM(temporal shift module),用来增强模型获取时间上下文信息的能力;最后,采用非局部操作模块non-local来提高模型提取关键位置信息的能力.实验结果表明,通过对YOWO模型的优化,使得网络的识别性能更佳.在学生课堂行为数据集上,改进后的算法的mAP值为 95.7%,相较于原YOWO算法在mAP值上提高了 4.6%;模型参数量为 81.97×106,计算量为 22.6 GFLOPs,参数量和计算量分别降低 32.3%和9.6%;检测速度为24.03 f/s,提升了约3 f/s.
Abstract
At present,since the recognition of most students'classroom behavior is mainly based on a single frame image and ignores behavior coherence,video information cannot be made full use of to accurately depict students'classroom behavior.Therefore,this study proposes an improved YOWO algorithm model to effectively employ video information to identify students'classroom behavior.First,this paper collects teaching videos from real classroom teaching in a university and produces an AVA format video dataset containing five types of students'classroom behavior.Second,the temporal shift module(TSM)is adopted to enhance the ability of this model to obtain time context information.Finally,a non-local operation module is utilized to improve the ability of the model to extract key location information.The experimental results show that by optimizing the YOWO model,the recognition performance of the network is better.In the classroom behavior dataset,the mAP value of the improved algorithm is 95.7%,4.6%higher than that of the original YOWO algorithm.The parameter number in the model is reduced by 32.3%at 81.97×106 and the calculation amount is decreased by 9.6%at 22.6 GFLOPs.The detection speed is 24.03 f/s,an increase of about 3 f/s.
关键词
YOWO算法/TSM/non-local/学生课堂行为/行为识别/注意力机制Key words
YOWO(you only watch once)algorithm/temporal shift module(TSM)/non-local/student classroom behavior/behavior recognition/attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据