基于人体姿态动态特征的跌倒行为识别方法

Falling Behavior Recognition Method Based on Dynamic Characteristics of Human Body Posture

意外跌倒严重威胁老年人健康安全,准确识别跌倒事件并及时予以报警可以有效降低跌倒者所受伤害.本文提出了一种新的跌倒识别方法,基于OpenPose深度卷积网络自图像提取的人体姿态关键点获取人体倾斜姿态动态特征,使用基于线性核的支持向量机完成跌倒行为二分类,并以基于人体下降姿态动态特征的阈值判断排除混淆性较大的非跌倒行为,保证算法召回率.本方法在人体动作数据集上测试取得了97.33%的准确率与94.80%的精确率,与现有基于图像的跌倒识别方法相比具有更优的性能,常见的单目RGB摄像机的特性使得本方法在普及性上优于需要Kinect相机的现有跌倒识别方法.

Accidental fall seriously threatens the health and safety of the elderly.Accurately identify the behavior of human falls and giving timely alerts are effective means to reduce the damage of accidental fall-wound.In this paper,we present a new fall detection method.In our method,dynamic characteristics of human tilt posture are extracted from the key points of the human body based on OpenPose deep convolutional network,the dynamic characteristics are then used for Linear SVM to detect falls,a judgment based on human descending posture is made to exclude confusing human behavior and improve the recall rate.Our method has achieved 97.33%accuracy and 94.80%precision on the human motion dataset,which is better than the current image-based falling behavior recognition method.Being suitable for monocular RGB camera make our method superior in practicality to the existing falling behavior recognition methods that require Kinect cameras.