基于Res2Net-YOLACT和融合特征的室内跌倒检测算法

为了加强对老年人的监护、降低跌倒带来的安全风险,提出了一种新的基于Res2Net-YOLACT和融合特征的室内跌倒检测算法.首先,通过融入Res2Net模块的YOLACT网络来提取视频图像序列中的人体轮廓;然后,利用两级判断的方法做出跌倒决策,其中一级判别通过运动速度特征粗略判断是否发生异常状态,二级通过融合人体形状特...

Indoor fall detection algorithm based on Res2Net-YOLACT and fusion feature

In order to strengthen the monitoring of old people and reduce the safety risks caused by falls， a new indoor fall detection algorithm based on Res2Net-YOLACT and fusion feature was proposed. For the video image sequences， firstly， the YOLACT network integrated with Res2Net module was used to extract the human body contour， and then a two-level judgment method was used to make a fall decision. In the first level， whether an abnormal state occurs was judged roughly through the movement speed feature， and in the second level， the human body posture was determined through the model structure that combines the body shape features and the depth feature. Finally， when fall posture was detected and the occurrence time was greater than the threshold， a fall alarm was given. Experimental results show that the proposed fall detection algorithm can extract the human body contour well in complex scenes， which has good robustness to illumination as well as a real-time performance of up to 28 fps （frames per second）. In addition， the classification performance of the algorithm after adding manual features is better， the classification accuracy is 98.65%， which is 1.03 percentage points higher than that of the algorithm with original CNN （Convolutional Neural Network） features.