重庆大学学报Issue(1)：12-17,6.DOI:10.11835/j.issn.1000-582X.2016.01.002
基于改进 SVM 分类器的动作识别方法
A gesture-recognition algorithm based on improved SVM
摘要
Abstract
An improved action recognition method is proposed based on the signals acquired by a smart phone acceleration sensor to reduce the complexity of the traditional action recognition method and enhance the recognition rate.The blind selection method is applied in feature extraction stage,which means using principal component analysis (PCA)method to reduce dimensionality and eliminate multi-dimensional interference,while the selected features have no corresponding physical significance.In classification and identification,the genetic algorithm is used to optimize support vector machine (SVM) classifier. Experimental results indicate that the proposed method can accurately recognize actions such as walking, standing,running and climbing stairs.关键词
小波去噪/PCA/遗传算法/SVM/动作识别Key words
wavelet denoising/PCA/genetic algorithm/SVM/action recognition分类
信息技术与安全科学引用本文复制引用
王见,陈义,邓帅..基于改进 SVM 分类器的动作识别方法[J].重庆大学学报,2016,(1):12-17,6.基金项目
中央高校基本科研业务费科研专项资助项目（CDJZR12110009）。Supported by Fundamental Research Funds for the Central Universities（CDJZR12110009）． （）
