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Elderly fall detection system based on multiple shape features and motion analysis

机译:基于多种形状特征和运动分析的老年人跌倒检测系统

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This paper presents an intelligent video-based fall detection system. First, the silhouette of a person is extracted using a background subtraction technique, then a set of features is measured to define if a fall happened, for that a new technique is presented to estimate the head position, and a finite state machine (FSM) is used in the aim to compute the vertical velocity of the head. This algorithm is tested on the L2ei dataset where more than 2700 frames have been labelled in order to train three different classifiers. The results show that our system can predict the correct class with an accuracy that can reach up to 99.61% with a maximum global error of 1.5%.
机译:本文提出了一种基于视频的智能跌倒检测系统。首先,使用背景减法技术提取人的轮廓,然后测量一组特征以定义是否发生了跌倒,为此提出了一种新技术来估计头部位置,并使用了有限状态机(FSM)用于计算头部的垂直速度。该算法在L2ei数据集上进行了测试,其中已标记了2700多个帧,以训练三个不同的分类器。结果表明,我们的系统可以以高达99.61%的精度预测正确的类别,最大全局误差为1.5%。

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