吉林大学学报（理学版）2016，Vol.54Issue(4)：845-851,7.DOI:10.13413/j.cnki.jdxblxb.2016.04.30
基于跨域字典学习算法的人体行为识别
Human Action Recognition Based on Cross-Domain Dictionary Learning Algorithm
摘要
Abstract
A cross-domain dictionary learning algorithm was applied to the recognition of human actions.By introducing the auxiliary domain data set,the algorithm learned from the original training set (target domain)together with the dictionary to obtain the dictionary pair,and then obtain the sparse coding of the action class,and effectively expanded diversity of training set.The algorithm is a combination of learning framework of dictionary learing and training classification,which can be used to classify the reconstruction errors in the learning process.The experiment was carried out under the condition of MATLAB simulation.The UCF YouTube data sets were regarded as the original training set,and the HMDB5 1 data sets were regarded as an auxiliary domain data set.7 coherent human actions of the two data sets were selected to do the work of recognition according to the flow chart of the proposed algorithm.The cross-domain dictionary learning algorithm was compared with another two kinds of human actions recognition algorithms.The results show that the recognition rate of the proposed algorithm is significantly improved.It is proved that the cross-domain dictionary learning algorithm is effective in human actions recognition.关键词
人体行为识别/密集点轨迹/跨域字典学习/稀疏编码Key words
human action recognition/dense point trajectory/cross-domain dictionary learning/sparse representation分类
信息技术与安全科学引用本文复制引用
张冰冰,梁超,倪康,史东承..基于跨域字典学习算法的人体行为识别[J].吉林大学学报（理学版）,2016,54(4):845-851,7.基金项目
吉林省教育厅“十三五”规划项目(批准号:吉教科合字[2016]第349号) （批准号:吉教科合字[2016]第349号）
