Gesture Recognition Based on Hidden Markov Model from Sparse Representative Observations

摘要：

　　Hand gesture recognition plays an important role in human computer interaction,virtual reality,and so on.In this paper,we focus on how to generate efficient observations after feature extraction in Hidden Markov model (HMM).Vector quantization such as kmeans clustering algorithm is usually applied to generate codebooks in HMM-based methods.Unlike traditional vector quantization,we use sparse coding (SC) and HMM to achieve the task of hand gesture recognition,which we call ScHMM.Sparse coding provides a class of algorithms for finding succinct representations of stimuli.In the training stage,feature-sign search algorithm and Lagrange dual are applied to obtain codebook and in the testing stage,feature-sign algorithm is used to get efficient observations.We evaluated our method on public database.ScHMM compares favorably to state-of-the-art methods,namely HMM,conditional random fields,hidden conditional random fields and latent dynamic conditional random fields.

关键词： hand gesture recognition HMM sparse coding Kmeans vector quantization

作者: Jun Wan Qiuqi Ruan Gaoyun An Wei Li

作者单位: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China

会议类型: 国际会议

会议名称: 2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)

会议地点: 北京

会议语种:英文

页码: 1180-1183

在线出版日期: 2012-10-21（万方平台首次上网日期，不代表论文的发表时间）