一种基于静态和动态特征的步态识别新方法

最近,利用步态对个人身份进行识别受到越来越多生物识别技术研究者的重视。步态能量图(Gait EnergyImage,GEI)是一种有效的步态表征方法。把步态能量图分解为身体相关能量图(Body-Related GEI,BGEI)、步态相关能量图(Gait-Related GEI,GGEI)、身体步态相关能量图(Body-Gait-Related GEI,BGGEI)3部分,利用傅立叶描绘子对身体相关能量图(BGEI)、身体步态相关能量图(BGGEI)进行描述,利用Gabor小波提取步态相关能量图(GGEI)的幅值特征,分别研究了它们的识别能力,并在Rank层和Score层融合步态相关能量图(GGEI)、身体步态相关能量图(BGGEI)这两部分信息用于步态识别。该算法在CASIA数据库上进行的试验取得了较高的正确识别率。     

基于傅立叶描绘子的步态识别

步态识别作为一种新的生物特征识别技术，通过人走路的姿势实现对个人身份的识别和认证。利用傅立叶描绘子对步态轮廓图像进行描述，用步态图像的高宽比进行步态的准周期性分析，并采用动态时间规正算法解决不同的步态周期的图像序列之间的比较问题。该算法在CMU数据库上面进行试验取得了较高的正确识别率。     

傅里叶变换的多视角步态识别

步态识别作为一种全新的生物特征识别技术,通过人走路的姿势实现对个人身份的识别和认证。步态能量图将一个周期的步态组合在一起,增强了各帧的相关性,减少了噪声的干扰。对步态能量图进行傅里叶变换,利用傅里叶变换的低频分量对多个视角的步态进行识别。在CASIA数据库中进行实验,结果表明算法简单快速,取得了较好的识别效果。     

基于人类行走模型的步态特征提取方法研究

步态分析与评价是人类保健和康复的重要研究内容,运动学分析方法是步态分析中最有效最准确最直接的方法,因而被广泛采用.步态特征的提取是运动学分析方法的关键技术,也是进一步进行步态分析的基础.首先给出了人类行走的三维模型,接着提出一套从人类行走的运动学数据中提取相关步态特征的方法,包括步态周期特征和脚印特征的提取方法,最后对20到60岁年龄段的44个人的运动学数据进行分析,实验结果证明这套方法具有很高的准确性和实用性.     

集成HOG步态模板

步态模板在提升步态识别的实时性能中扮演了关键角色。由于缺乏时间信息和不能充分提取步态中的统计特征,其识别性能会受到一定的损害。以步态能量图（GEI）为模板,并使用基于时间保持的步态能量图（CGI）,从这两个模板中进一步提取空间特征。在此基础上,构造了集成HOG步态模板。这一模板能较好地保持时间信息和有效地提取空间结构特征。在USF步态数据集的实验表明,与其他已知步态识别方法相比,提出的模板实现了好的识别性能。     

Human gait assessment using a 3D marker-less multimodal motion capture system

Multimedia Tools and Applications - Gait analysis is the measurement, processing and systematic interpretation of biomechanical parameters that characterize human locomotion. It supports the...     

基于SVM的步态识别方法综述

步态识别是非接触式生物特征识别领域的前沿课题,通过对人体行走方式的识别以确定个体的身份,在智能视频监控领域有较高的研究价值.步态分类是步态识别过程中的重要任务和关键步骤.首先概述了步态识别过程及分类方法,然后重点对基于支持向量机的步态分类方法进行了综述,分析了基于该方法的最新研究进展,对每个具体研究方法的优缺点进行了对比.最后,指出目前步态识别在实际应用中存在的局限性,并对该领域发展方向进行了展望.     

Parkinsonian gait patterns quantification from principal geodesic analysis

Pattern Analysis and Applications - Parkinson is a neuromotor disease caused by dopamine deficiency that produces progressive alterations in locomotion. Gait analysis is a primary alternative to...     

Sparse multilinear Laplacian discriminant analysis for gait recognition

Pattern Analysis and Applications - Distinct and unique ways of walking have attracted the attention of researchers for identification of different individuals. The Gait; the walking video sequence...     

Enhancing the robustness of forensic gait analysis against near-distance viewing direction differences

Multimedia Tools and Applications - Gait analysis is a promising biometric technology to visually and quantitatively analyze an individual’s walking style. In Japan, silhouette-based...     

Gait recognition based on Gabor wavelets and (2D)2PCA

Multimedia Tools and Applications - Gait recognition is one of the most important techniques in application areas such as video-based surveillance, human tracking and medical systems. In this...     

Effect of silhouette quality on hard problems in Gait recognition.

Gait as a behavioral biometric has been the subject of recent investigations. However, understanding the limits of gait-based recognition and the quantitative study of the factors effecting gait have been confounded by errors in the extracted silhouettes, upon which most recognition algorithms are based. To enable us to study this effect on a large population of subjects, we present a novel model based silhouette reconstruction strategy, based on a population based hidden Markov model (HMM), coupled with an eigen-stance model, to correct for common errors in silhouette detection arising from shadows and background subtraction. The model is trained and benchmarked using manually specified silhouettes for 71 subjects from the recently formulated HumanID Gait Challenge database. Unlike other essentially pixel-level silhouette cleaning methods, this method can remove shadows, especially between feet for the legs-apart stance, and remove parts due to any objects being carried, such as briefcase or a walking cane. After quantitatively establishing the improved quality of the silhouette over simple background subtraction, we show on the 122 subjects HumanID Gait Challenge Dataset and using two gait recognition algorithms that the observed poor performance of gait recognition for hard problems involving matching across factors such as surface, time, and shoe are not due to poor silhouette quality, beyond what is available from statistical background subtraction based methods.     

GCA-Net: Gait contour automatic segmentation model for video gait recognition

Multimedia Tools and Applications - Gait recognition from videos is a very important task for surveillance video analysis. Although a number of studies have explored gait recognition models, they...     

Human gait recognition using localized Grassmann mean representatives with partial least squares regression

Multimedia Tools and Applications - Gait recognition has become popular due to the rising demand for nonintrusive biometrics. At its nascent stage of development, gait recognition faces a number of...     

Simple and efficient pose-based gait recognition method for challenging environments

Pattern Analysis and Applications - Gait is a biometry characterized by the identification of individuals by the way they walk. It is recently gaining evidence because it can be collected at...     

基于增强能量图和二维保局映射的行为分类算法

行为分类中,现有的特征提取要么方法简单、识别率低,要么特征提取复杂、实时性差。对此,提出一种算法：将步态能量图（GEI）改进,得到增强步态能量图（EGEI）;然后将二维保局映射（2DLPP）应用于特征空间降维;最后采用最近邻(NN)法分类。EGEI比GEI更能反映目标特征;2DLPP降维效果好于主成分分析(PCA)及一维保局映射。在Weizmann行为数据库上测试,实验结果表明：该算法简单、准确率高,平均识别率达到了91.22%。     

Knee and torso kinematics in generation of optimum gait pattern based on human-like motion for a seven-link biped robot

Multibody System Dynamics - Gait pattern affects the quality of a walking robot. In this paper, an optimum human-like gait generation method is proposed for a seven-link biped robot. This method...     

步态识别技术方法研究

步态识别是依据人行走方式的不同对人的身份进行识别的一种生物特征识别技术。介绍了步态识别现状及实现方法,并给出了前景展望。     

基于步态能量图和不变矩的身份识别算法

分析步态能量图即具有作为静态的外观特征,又包含了识别的动力学有用信息,同时证明了步态能量图对噪声的不敏感性。文章提出了一种基于步态能量图和不变矩的身份识别算法,介绍了不变矩的基本理论以及Hu提出的七个不变矩,利用图像不变矩的平移、尺度和旋转不变特性,从原始的步态能量图中提取不变矩特征作为步态能量图的输入特征向量,运用不变矩的最小距离分类器的模式匹配进行步态特征分类。最后在CASIA步态数据库上对所提出的算法和其他新的步态识别方法相比较。实验结果表明,提出的算法是一种有效的步态识别方法。     

Gait recognition based on joint distribution of motion angles

Gait as a biometric trait has the ability to be recognized in remote monitoring. In this article, a method based on joint distribution of motion angles is proposed for gait recognition. The new feature of the motion angles of lower limbs is defined and extracted from either 2D video database or 3D motion capture database, and the corresponding angles of right leg and left leg are joined together to work out the joint distribution spectrums. Based on the joint distribution of these angles, we build a feature histogram individually. In the stage of distance measurement, three types of distance vector are defined and utilized to measure the similarity between the histograms, and then a classifier is built to implement the classification. Experiments has been carried out both on CASIA Gait Database and CMU motion capture database, which show that our method can achieve a good recognition performance.