基于人体姿态估计的手机使用状态监控

智能手机的日益普及给人们带来便捷的同时也带来了不少的隐患,在一些特定的场景下需要对手机的使用进行监控和限制.本文设计了一套手机使用状态监控系统,先采用YOLOv3检测图像中的人体,然后通过Open Pose姿态估计算法获得人体关节点,再通过YOLOv3判断手部区域是否有手机,最后通过神经网络分类器识别当前的手机使用状态.系统的应用测试表明该方案具有良好的检测与识别效果,能够满足相关场景的应用需求.

Phone Usage Monitoring Based on Human Pose Estimation

The increasing popularity of smartphones brings not only convenience to people but also a lot of hidden dangers. Thus, it is necessary to monitor and restrict the use of phones in some specific situations. In this paper, we design a system to monitor phone usage. First, YOLOv3 is used to detect human bodies in an image. Then, the joints for each person are obtained by the OpenPose pose estimation algorithm. Furthermore, YOLOv3 is employed to judge whether there is a mobile phone in the hands. Finally, the current phone usage status is recognized by a neural network classifier. The experimental results show that the proposed scheme has good detection and recognition performance and can meet the application requirements in relevant scenarios.