摘要
目前基于深度学习的步态识别方法大多通过叠加卷积层获取全局特征,忽略有利于细粒度分类的局部特征.针对上述问题,文中提出结合非局部与分块特征的跨视角步态识别方法.将一对步态能量图(GEI)作为输入,提取单样本的非局部信息与样本对之间的相对非局部信息.为了更好地提取局部特征,根据GEI的几何特性,将人体区域水平切分为静态块、微动态块和强动态块,连接至3个二值分类器分别进行训练.在OUISIR-LP和CASIA-B步态数据集上的对比实验表明,文中方法的正确识别率较高.
In most existing gait recognition methods based on deep learning, global features are acquired by stacking convolutional layers, and local features beneficial to fine-grained classification are ignored.Aiming at this problem, a cross-view gait recognition method is proposed by combining non-local and part-level features.A pair of gait energy images(GEIs) is used as input to extract the non-local information of a single sample and the relative nonlocal information of the sample pairs.Then, human body regions are divided horizontally into static blocks, microdynamic blocks and strong dynamic blocks to extract better local features according to the geometric characteristics of GEI.Furthermore, the segmented regions are connected to three binary classifiers for training respectively. Finally, experiments on OU-ISIR-LP and CASIA-B gait datasets show that the proposed method produces a higher correct recognition rate.
作者
冯世灵
王修晖
FENG Shiling;WANG Xiuhui(College of Information Engineering,China Jiliang University,Hangzhou 310018)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第9期821-827,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61602431,61303146)资助~~
关键词
步态识别
跨视角识别
非局部特征
分块特征
Gait Recognition
Cross-View Recognition
Non-local Features
Part-Level Features
