基于深度学习的电子考场作弊行为识别技术

Electronic Examination Cheating Behavior Recognition Technology Based on Deep Learning

为确保考试的公正性,并解决现有电子监考系统无法自动解析监控视频、人工审查视频劳动强度过高的问题,文中提出了一种基于深度学习的考生作弊行为识别方法。该方法构建了一个用于深度学习训练的数据集,结合物体识别算法YOLO和人体姿态估计工具OpenPose,使用帧间差分技术快速提取关键帧,通过分析视频帧来定位和标记可疑考生,提高了检测的准确率和速度。

To ensure the fairness of the exam and solve the problems of existing electronic invigilation systems not being able to automatically parse surveillance videos and the high labor intensity of manually reviewing videos,this paper proposes a deep learning based method for identifying cheating behavior among candidates.This method constructs a dataset for deep learning training,combined with object recognition algorithm YOLO and human pose estimation tool OpenPose,using inter frame difference technology to quickly extract keyframes.By analyzing video frames to locate and mark suspicious candidates,the accuracy and speed of detection are improved.