一种基于 CNN 和人体椭圆轮廓运动特征的 摔倒检测方法

为了解决传统的使用几何特征检测摔倒的方法的不稳定、难于区别一些相似的活 动等问题，提出了一种基于卷积神经网络(CNN)和人体椭圆轮廓的运动特征的摔倒检测方法。 首先，使用高斯混合模型检测出人体目标并求出其最小外接椭圆轮廓。然后在每一帧的椭圆 轮廓中提取出长短轴之比、方向角和人体质心的竖直方向速度，融合成一个基于时间序列的 运动特征。最后，经过一个浅层的 CNN 对这些运动特征进行训练，用于摔倒判断，并区分相 似的活动。实验结果表明，本文方法和现有的方法相比，克服了几何特征的不稳定性，提高 了检测率。

A Fall Detection Method Based on CNN and Motion Features of Human Elliptical Contour

In order to solve the problems of the instability of the traditional method of using geometric features to detect falls, and  the difficulty to distinguish some similar activities, a fall detection method based on convolution neural network (CNN) and the motion features of the elliptical contour of  human body is proposed. First, this method uses the Gauss mixture model to detect the human target and find out the minimum external elliptical contour. Then, the three features of the long and short axis ratio, the orientation angle and the vertical velocity of the human body’s centroid extracted in each frame’s elliptical contour, are fused into a motion feature based on time series. Last, a shallow CNN is then trained to detect falls and distinguish some similar activities. Experiment results show that our method overcomes the instability of geometric features and therefore enhances the detection rate compared with the existing methods.