基于图像语义分割和CNN模型的老人跌倒检测

随着老龄化社会的到来，独居老人的安全问题越来越引人关注.其中，跌倒是老人在家中最常见也是危害最大的风险之一.当前已经有许多关于老人跌倒检测的算法，它们大多应用在摄像头固定的场景下，并主要采用前景提取方法来获取人体轮廓.采用固定摄像头意味着需要为家中每一处独立的空间都安装监控设备才能保证对于老人的全面监控，这显然不实用.基于此，本文采用图像语义分割算法和CNN分类模型，提出了一种可用于移动摄像头上的老人跌倒检测算法.首先采用当前流行的全卷积神经网络（fully convolutional network）语义分割算法^1]分割出图像中的人体，对于满足面积比例条件的情况，直接通过宽高比特征判断人体是否处于跌倒状态；否则，提出一种融合的CNN人体姿态判别模型，将人体区域分成Stand、Fall、Half-Lying三种情况分别进行检测，最后根据三者的分类结果判定图像中是否包含跌倒人体.实验结果显示，文中的算法在具有较高的识别准确率（91.32%）的同时，具有较低的误报率（1.66%）.

Elderly Falling Detection Based on Image Semantic Segmentation and CNN Model

With the growing population of elderly people, the safety of the elders living alone becomes a rising issue for the society. Falling down is one of the most common and greatest risks and injuries occurring to the elders living at home. There have been many algorithms on elderly falling detection. However, the vast majority of the existing methods, which use foreground extraction to get human body silhouette are implemented on static cameras. It means that we should implement cameras for every independent region in the house to make sure that the elders is visible in the frame, which is impractical. This paper proposes a novel approach for detecting human body falls based on image semantic segmentation and convolutional neural network model(CNN), which can be implemented on portable cameras. First, the fully convolutional network(FCN) is used to segment human pixels in the frame. If the body shape meets the conditions of area ratio, aspect ratio is used to estimate whether it is a falling body or not. Otherwise, a combined CNN classification model is used. Regions of human body are classified in three cases (fall, stand, half-lying) and the results are used to estimate whether there is a falling body in the frame. From the experimental results we achieved, it was concluded that our method has a high recognition rate (91.32%) and low false alarm rate(1.66%).