西安理工大学学报2017，Vol.33Issue(2)：169-174,6.DOI:10.19322/j.cnki.issn.1006-4710.2017.02.008
基于时间维度局部特征的人体行为识别
Local feature extraction using time domain information for human action recognition
摘要
Abstract
In the human action recognition application,the traditional 3D template convolution method is time-consuming and difficult to avoid defects of pseudo interest points of background.To overcome this weak point,we propose a motion human local interest point detect method combining motion information and FAST feature.First,the difference is computed on every two adjacent frames,and the FAST(Features From Accelerated Segment) feature point detection is deployed on the two pieces of motion information by taking intersection of the two points set as final output with non-maximum suppression.With the low time-consuming FAST algorithm applied,this method should be an efficient motion intersection point detector with high accuracy and motion correlation.Finally,we use the BOW model to generate action feature vector.The classifier used is SVM (Support Vector Machine),KNN,Decision Tree and LDA.Performance is tested on deferent datasets,the simple KTH and Weizmann,SVM classifier,obtaining the best accuracy with KNN being more efficient.关键词
行为识别/局部特征/运动信息/FAST角点/词袋模型Key words
action recognition/local feature/motion information/FAST corner/BOW(Bag of Words) model分类
信息技术与安全科学引用本文复制引用
张九龙,张镇东,杨夙,高阳,肖照林..基于时间维度局部特征的人体行为识别[J].西安理工大学学报,2017,33(2):169-174,6.基金项目
国家自然科学基金资助项目(61402362) （61402362）
陕西省自然科学基金资助项目(2015JQ6218,2016JQ6069) （2015JQ6218,2016JQ6069）
陕西省教育厅专项科研计划资助项目(16JK1553) （16JK1553）
西安市碑林区科技计划资助项目(GX1616) （GX1616）
