面向嵌入式系统的人体行为识别

Human Behavior Recognition for Embedded System

为了在嵌入式平台上使人体行为识别网络达到实时效果,提出了一种基于轻量级OpenPose模型的人体行为识别方法。所提方法从人体的18个骨骼关键点角度出发,通过骨骼关键点的空间位置确定行为类型。首先通过轻量级OpenPose模型提取人体的18个骨骼关键点坐标信息,然后利用关键点的编码对人体的行为进行描述,最后利用分类器对获取的关键点坐标进行分类,从而识别出人体的行为状态,并将其移植到Jetson Xavier NX设备上,利用单目相机进行了测试。实验结果表明,所提方法在嵌入式开发板Jetson Xavier NX上能够快速、准确识别出行走、挥手、下蹲等人体的11类行为,平均识别准确率达到96.08%,检测速度达到了11 frame/s以上,相比于原模型,检测速度提升了177%。

To achieve real-time effects of the human behavior recognition network on the embedded platform,a human behavior recognition technique based on the lightweight OpenPose model is proposed.This approach begins with the viewpoint of 18 human body bone key points and calculates the behavior type based on the spatial position of the bone key points.First,the lightweight OpenPose model is used to extract the 18 bone key points to coordinate information about the human body.Then,the key point coding is used to describe the human body behavior.Finally,the classifier is used to classify the acquired key point coordinates to detect the human body behavior status and transplant it into Jetson Xavier NX equipment using a monocular camera for testing.Experimental results show that this method can quickly and accurately identify 11 types of human behaviors,such as walking,waving,and squatting,on the embedded development board Jetson Xavier NX,with an average recognition accuracy rate of 96.08%,and detection speed of>11 frame/s.The frame rate is increased by 177%compared to the original model.