基于卷积神经网络和多判别特征的跌倒检测算法

A Fall Detection Algorithm Based on Convolutional Neural Network and Multi-Discriminant Feature

针对传统跌倒检测算法中特征提取不充分、跌倒判别条件泛化性差、实时性差等问题,提出一种基于卷积神经网络和多判别特征的跌倒检测算法.为了完成更丰富的特征信息提取并保证实时性,首先,使用MobileNetV3轻量级网络完成对输入图片中人物特征信息的准确、快速提取;其次,使用3个小型卷积核的叠加和残差网络,保证网络在具有相同感受野的情况下降低网络模型的参数量,以保证图像中人体关键点检测的实时性;再次,为了提高跌倒状态判别的准确性,将人体躯干、四肢与地面间夹角,以及人体标定框高宽比变化作为跌倒判别特征;最后,设计了一个基于云服务器的物联网系统,以缓解用户终端计算能力不足导致实时性差的问题.在URFD数据集和自建数据集上进行大量实验的结果表明,该算法的检测准确率分别为99.0%和98.5%,该算法相对于传统跌倒检测算法具有更高的准确性和更好的普适性.

In order to solve the problems that insufficient feature extraction,poor generalization of fall dis-crimination conditions,and poor real-time performance in traditional algorithms,a fall detection algorithm based on convolutional neural network and multi-discriminant features is proposed.In order to complete the extraction of richer feature information and ensure real-time performance,firstly,the MobileNetV3 light-weight network is used to complete the accurate and fast extraction of the character feature information in the in-put image.Secondly,the superposition of three small convolution kernels and the residual network are used to reduce the number of parameters of the network model in the case of the same receptive field,so as to guarantee the real-time detection of human key points in the image.In order to improve the accuracy of fall state discrimination,the angle between human torso,limbs and the ground,and the change of the height-to-width ratio of the human calibration frame,are used as fall discrimination features.Finally,an internet of things system based on cloud server is designed to alleviate the problem of poor real-time per-formance caused by insufficient computing power of user terminals.A large number of experiments on the URFD dataset and self-built dataset show that the accuracy of the proposed algorithm is 99.0%and 98.5%,respectively,and the proposed algorithm has higher accuracy and better universality than the traditional fall detection algorithms.