基于嵌入式隐马尔可夫模型的步态识别  

Gait Recognition Based on Embedded Hidden Markov Model

作　　者：张前进[1] 徐素莉[1] 

机构地区：[1]河南科技大学电子信息工程学院,河南洛阳471003

出　　处：《信息与控制》2010年第1期25-29,共5页Information and Control

摘　　要：针对从多帧步态中更有效提取步态特征的问题,提出了一种基于嵌入式隐马尔可夫模型的步态识别算法.首先采用背景减除方法提取出人体的侧影轮廓,通过分析轮廓宽度向量的自相关性计算出步态的周期,并得到平均步态能量图.接着利用二维离散余弦变换获得平均步态能量图的空间特征信息,然后把能量图的观测块转化为观测向量实现了步态识别.最后运用最近邻法在两个不同的数据库上进行算法验证,实验结果表明该算法具有较好的识别性能.A gait recognition algorithm based on embedded hidden Markov model （e-HMM） is proposed for the problem of extracting features efficiently from a sequence of gait frames. First, body silhouette extraction is achieved by background subtraction method. Gait period is calculated through analyzing the silhouette-width-vector autocorrelation and the mean gait energy image （GEI） is obtained. Then, spatial feature information of the mean GEI is obtained by two dimensional discrete cosine transform （2D-DCT）. The mean GEI observation block is transformed into observation vector and gait recognition is performed. In the end, the nearest-neighbor method is adopted to confirm the proposed algorithm using two different gait databases. Experimental results show that the algorithm has higher recognition performance.

关 键 词：步态识别 特征提取 步态能量图 嵌入式隐马尔可夫模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]