摘要
针对采用单一描述子和单一特征编码方法导致三维人体骨架序列的行为识别率较低的问题,提出一种基于多描述子特征编码的方法。首先,从三维人体骨架序列中分别提取运动姿态描述子和角度描述子。然后,对每种描述子分别进行向量量化编码、稀疏编码和局部约束线性编码,从而获得六种特征。最后,根据这六种特征分别构造线性分类器,通过投票机制得到最终的识别结果。为了验证所提方法的有效性,在三维人体骨架序列行为数据集MSR Action3D上进行了实验,实验结果表明该方法的识别率为94.9%,并且高于其他方法的识别率。
Aiming at the problem of low activity recognition rate from 3D human skeleton sequence based on a single descriptor and a single feature coding method,we propose a method based on multi-descriptor feature coding. Firstly,moving pose descriptor and angle descriptor are extracted respectively from 3D human skeleton sequence. Then,vector quantization coding,sparse coding and locality constrained linear coding are employed respectively to get six kinds of feature based on two kinds of descriptor. Finally,linear classifiers are respectively constructed based on these six kinds of feature,and the recognition result is decided by voting strategy. In order to validate the effects of the proposed method,the experiment on MSR Action3D,a public 3D human skeleton sequence activity database,demonstrates that the proposed method achieves 94.9% of recognition accuracy,which is superior to the state-of-art of methods.
作者
宋相法
姚旭
SONG Xiang-fa;YAO Xu(School of Computer and Information Engineering,Henan University,Kaifeng 475004,Chin)
出处
《计算机技术与发展》
2018年第8期17-21,共5页
Computer Technology and Development
基金
国家自然科学基金(U1504611)
河南省教育科学技术研究重点项目(15A520010)
