数据采集与处理2016，Vol.31Issue(5)：890-902,13.DOI:10.16337/j.1004-9037.2016.05.005
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
摘要
Abstract
Under the background of global aging and empty nest family,the tumble of seniors has attrac-ted a great deal of attention.To provide help for seniors and relieve the inj ury of tumble,a tumble recog-nition 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 sub-traction 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 ex-perimenters′walking,siting down,squating down and tumbling as the experimental samples.The algo-rithm 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 9 5%.关键词
目标检测/特征提取/支持向量机/跌倒识别Key words
obj ect detection/feature extraction/support vector machine/fall recognition分类
信息技术与安全科学引用本文复制引用
彭玉青,高晴晴,刘楠楠,宋初柏,张媛媛..基于多特征融合的跌倒行为识别与研究[J].数据采集与处理,2016,31(5):890-902,13.基金项目
天津自然科学基金(13JCYBJC15400)资助项目 （13JCYBJC15400）
河北省高等学校科学技术研究重点(ZD2014030)资助项目。 （ZD2014030）
