计算机工程与应用2018，Vol.54Issue(7)：29-35,7.DOI:10.3778/j.issn.1002-8331.1712-0405
基于稀疏编码局部时空描述子的动作识别方法
Action recognition method based on sparse coding local spatio-temporal descriptors
摘要
Abstract
To overcome the slow training speed and low recognition rate of the existing action recognition algorithm,an action recognition method based on the sparse coding local spatio-temporal descriptor is proposed.The method firstly extracts the normal of the depth image,and uses the adaptive spatio-temporal pyramid based on the action energy to block the action frames.Then the local spatio-temporal descriptors are obtained by the local aggregation normal.The local spatio-temporal descriptors are encoded by sparse coding to get a set of dictionary vectors to reconstruct the sample data.Finally, the simplified Particle Swarm Optimization(sPSO)is used to optimize the SVM classifier to find the most suitable sam-ple data classification model.The experiment achieves a recognition rate of 93.80% and 95.83% on MSRAction3D and MSRGesture3D datasets, and the training speed is significantly improved compared with the previous methods, which proves the effectiveness and robustness of the method.关键词
动作识别/稀疏编码/简化粒子群/深度序列/局部时空描述子Key words
action recognition/sparse coding/simplified Particle Swarm Optimization(sPSO)/depth sequence/local spatio-temporal descriptors分类
信息技术与安全科学引用本文复制引用
赵晓丽,田丽华,李晨..基于稀疏编码局部时空描述子的动作识别方法[J].计算机工程与应用,2018,54(7):29-35,7.基金项目
国家自然科学基金(No.61403302) （No.61403302）
西安交通大学基本科研业务费(No.XJJ2016029). （No.XJJ2016029）
