基于主运动轮廓线的步态表示与识别

提出了一个基于步态主运动轮廓线构造特征矩阵, 并进行特征表示和分类识别的算法. 该算法首先从步态轮廓线提取三段代表人体主要运动的部分, 基于它们到质心的横向距离构造描述步态图像序列的三个特征矩阵. 然后, 采用主分量分析(Principal component analysis, PCA)方法去除特征矩阵中的冗余数据, 并利用多元判别分析(Multiple discriminant analysis, MDA)将特征矩阵投影到更易于分类的空间. 最后, 在USF步态数据库上计算测试对象的Rank n识别率, 并与其他三个有代表性的算法进行比较. 实验结果显示, 本文算法的平均识别率更高, 抗干扰性更强.

Gait Expression and Recognition Based on Primary Motion Contours

This paper introduces a gait expression and recognition algorithm based on the feature matrix constructed from the primary motion contours of gait. Three segments are extracted from silhouette contours to represent primary motions of gait. Three feature matrices are constructed based on the horizontal distances from the segmented curves to the silhouette centroid. Principal component analysis (PCA) and multiple discriminant analysis (MDA) are utilized to reduce redundant data and separate different classes, respectively. The proposed algorithm is evaluated on USF dataset, and its performance is compared with three typical algorithms. Experimental results show that this algorithm has a higher mean recognition rate and achieves better performance in robustness than the other algorithms.