基于多特征融合的跌倒行为识别与研究  

Fall Behavior Recognition Based on Multi-feature Fusion

作　　者：彭玉青[1] 高晴晴[1] 刘楠楠[1] 宋初柏 张媛媛[1] 

机构地区：[1]河北工业大学计算机科学与软件学院,天津300401

出　　处：《数据采集与处理》2016年第5期890-902,共13页Journal of Data Acquisition and Processing

基　　金：天津自然科学基金(13JCYBJC15400)资助项目;河北省高等学校科学技术研究重点(ZD2014030)资助项目

摘　　要：在全球老龄化和空巢家庭的社会背景下,老年人的跌倒已成为当今社会备受关注的问题,为了能及时为老年人提供帮助,减轻摔倒带来的伤害,提出了一种基于图像处理的多特征融合跌倒识别算法。针对前景提取,本文提出了一种三帧差分法与背景减除法加权结合的目标提取算法,进而提取出目标轮廓的高度、宽高比、质心、矩形周长、Hu矩及Zernike矩特征;以行走、坐下、蹲下和跌倒4种行为数据作为样本,最后通过参数优化后的支持向量机训练及预测来实现跌倒的检测与识别。实验结果表明,所提出的算法不仅有效而且速度快、易于实现,平均识别率超过了95%。Under the background of global aging and empty nest family,the tumble of seniors has attracted a great deal of attention.To provide help for seniors and relieve the injury of tumble,a tumble recognition algorithm based on image processing and multi-features fusion is proposed.In view of the prospect of extraction,we propose an algorithm that combines three-frame difference method and background subtraction division of target by weight,then extract the height,ratio of width to height,the center of mass,the perimeter of a rectangle,Hu moments′and Zernike moments of target contour,using five experimenters′walking,siting down,squating down and tumbling as the experimental samples.The algorithm realizes tumble detection and recognition by training and predicting support vector machine（SVM）after parameter optimization.The experimental results show that the proposed algorithm is efficient and fast with easy implementation.The average recognition rate is more than 95%.

关 键 词：目标检测 特征提取 支持向量机 跌倒识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]