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

作　　者：赵晓丽 田丽华[1] 李晨[1] ZHAO Xiaoli;TIAN Lihua;LI Chen(School of Software Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]西安交通大学软件学院,西安710049

出　　处：《计算机工程与应用》2018年第7期29-35,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61403302);西安交通大学基本科研业务费(No.XJJ2016029)

摘　　要：针对已有动作识别算法训练速度慢且识别精度不高等问题,提出了基于稀疏编码局部时空描述子的动作识别方法。该方法首先对深度图像进行法线提取,同时应用基于运动能量的自适应时空金字塔对动作帧分块;然后局部聚集法线,得到显著性局部时空描述子;对局部时空描述子进行稀疏编码得到一组字典向量来重构样本数据;最后利用简化粒子群(sPSO)优化SVM分类器找到最适合样本数据的分类模型。实验在MSRAction3D和MSRGesture3D公开数据集上达到了93.80%和95.83%的识别率,且训练速度较传统方法有明显提升,证明了该方法的有效性和鲁棒性。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 spatiotemporal 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 sample 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 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]