基于流形学习的三维步态鲁棒识别方法

3D Robust Gait Recognition Based on Manifold Learning

针对过去几乎都是在单目视觉的情况下进行步态识别研究的现状,提出一种基于立体视觉的步态识别方法.首先利用立体匹配技术获得人体轮廓的三维信息,并据此构造出三维人体轮廓描述子以获取人体的步态特征.接着通过平滑、去噪等预处理手段抑制噪声的影响,并采用流形学习构建低维流形进行特征降维.最后将最近邻分类器和最近邻模板分类器用于识别过程.采用该方法在PRLABⅡ立体步态数据库和不规则测试数据集ExN上进行实验,获得较高的识别率.实验结果表明,文中所提出的方法具有与行人行走路径到摄像机之间的距离无关的特点,且对于不完整的残缺步态序列、行人行为姿态的变化、携带物品和服饰变化等具有较强的鲁棒性.

Aiming at the situation that many approaches for gait recognition are based on a single camera, an approach of gait recognition based on stereo vision is proposed. Firstly, 3D coordinates of human body contour are gotten by stereo matching. Then, 3D body contour descriptor （3D-BCD） is constructed to get the gait feature of human. The noise and glitch are eliminated by noise-eliminated method. Thus, manifold learning （Laplacian Eigenmaps ） is used for dimensionality reduction. The nearest neighbor classifier （NN） and the nearest neighbor classifier about template （TNN） are used for classifying category. Finally, a series of experiment results on the stereo gait database of PRLAB 11 and the irregular test stereo gait dataset ExN proved out the high correct classification rate and the strong robustness of the proposed approach. And the approach is not related with the distance between camera and the walking path. Moreover, it has stronger robustness with the incomplete gait sequences, the changes of human behavior, the changes of apparels, and carrying a bag.