基于改进姿态估计算法的嵌入式平台实时跌倒检测

为了实现视频中特殊人群跌倒检测的实时性和降低误检率。通过采用改进的姿态估计网络提取人体关节点的方法,研究了使用前后帧关节点的变化来对人体进行追踪和跌倒行为检测。为了在嵌入式平台上使姿态估计网络达到实时效果,采用带有注意力机制的轻量化结构搭建深度卷积网络来提取人体关节点坐标,并合成完整的骨架信息。结果表明：带有注意力机制的姿态估计算法在不同数据集上的准确度均有提升；同时在嵌入式平台上保持误检率较低的情况下达到实时跌倒检测。可见基于改进姿态估计算法并通过关节点判断的方法较好地实现了人体的跌倒检测。

Real-Time Fall Detection for Embedded Platform Based on Improved Pose Estimation Algorithm

In order to achieve real-time fall detection of special people in the video and reduce the false detection rate. By using the improved pose estimation network to extract the human body key points, the changes of the frame key points before and after were studied to track the human body and detect the fall behavior. In order to achieve the real-time effect of the pose estimation network on the embedded platform, a lightweight structure with attention mechanism was used to build a deep convolution network to extract the joint coordinates of the human body and synthesize the complete skeleton information. The results show that the accuracy of the pose estimation algorithm with attention mechanism is improved on different datasets. At the same time, the real-time fall detection is achieved with low error detection rate on the embedded platform. It can be seen that the improved pose estimation algorithm and the nodal judgment method can better realize the fall detection of human body.