基于关节点特征的跌倒检测算法

Fall detection algorithm based on joint point features

针对跌倒检测算法中存在网络计算量大和类跌倒行为难以区分的问题,提出一种基于关节点特征的跌倒检测算法。首先,在目前先进的CenterNet算法基础上提出了深度可分离卷积CenterNet(DSC-CenterNet)关节点检测算法,从而在减少骨干网络计算量的同时准确检测人体关节点并获取关节点坐标;然后,基于关节点位置和人体先验知识来提取可充分表达跌倒行为的空间特征和时间特征作为关节点特征;最后,把关节点特征向量输入全连接层,并经Sigmoid分类器输出跌倒或非跌倒两种类别,从而实现人体目标的跌倒检测。实验结果表明,所提算法在UR Fall Detection数据集上对不同状态变化下跌倒检测的平均准确率达到98.00%,区分类跌倒行为的准确率达到98.22%,跌倒检测速度为18.6 frame/s。与原CenterNet结合关节点特征跌倒检测的算法相比,DSC-CenterNet结合关节点特征算法的跌倒检测速度提升了22.37%,提高后的速度可有效满足视频监控下人体跌倒检测任务的实时性。该算法能有效提高跌倒检测速度并对人体跌倒状态进行准确检测,且进一步验证了基于关节点特征的跌倒检测算法在视频跌倒行为分析中的可行性与高效性。

In order to solve the problems of large amount of network computation and difficulty in distinguishing falling-like behaviors in fall detection algorithms,a fall detection algorithm based on joint point features was proposed.Firstly,based on the current advanced CenterNet algorithm,a Depthwise Separable Convolution-CenterNet(DSC-CenterNet)joint point detection algorithm was proposed to accurately detect human joint points and obtain joint point coordinates while reducing the amount of backbone network computation.Then,based on the joint point coordinates and prior knowledge of the human body,the spatial and temporal features expressing the fall behavior were extracted as the joint point features.Finally,the joint point feature vector was input into the fully connected layer and processed by Sigmoid classifier to output two categories:fall or non-fall,thereby achieving the fall detection of human targets.Experimental results on UR Fall Detection dataset show that the proposed algorithm has the average accuracy of fall detection under different state changes reached 98.00%,the accuracy of distinguishing falling-like behaviors reached 98.22%and the fall detection speed of 18.6 frame/s.Compared with the algorithm of the original CenterNet combining with joint point features,the algorithm of DSC-CenterNet combining with joint point features has the average detection accuracy increased by 22.37%.The improved speed can effectively meet the realtime requirement of the human fall detection tasks under surveillance video.This algorithm can effectively increase fall detection speed and accurately detect the fall state of human body,which further verifies the feasibility and efficiency of fall detection algorithm based on joint point features in the video fall behavior analysis.