线性插值框架下矩阵步态识别的性能分析

针对现有的步态周期检测方法检测效果不佳以及行走速度变化对步态识别性能有很大影响的问题,提出的基于矩的步态周期检测方法中,Zernike矩需要人体居中、尺度归一的前期预处理过程,而伪Zernike矩具有能描述运动图像的特点,它可以避免人体居中、尺度归一等处理,以便直接测试步态的周期性.根据行走时的两帧之间的特征取决于前一帧和后一帧的特征,提出了基于线性插值的矩阵步态识别算法框架,并且将投影特征、Hough变换特征、Trace变换特征和Fan-Beam映射特征应用在CASIA(B)步态库上,验证了框架的有效性,为解决步态识别问题带来新的方法与思路.这种基于线性插值的矩阵步态识别特征本质上是一种权值不同的能量形式.     

基于新时空融合的步态轮廓分割算法

从人体步态图像视频序列中,提取完整的人体区域是人体运动步态识别的一个重要环节.提出一种新的人体运动目标分割算法,无需小波反变换.结合背景减除法和帧间差分法所得到的二值结果来进行运动估计,对当前帧图像采用一阶小波变换,利用高阶线性插值算法将小波变换的LL分量扩展与当前帧图像同样的大小,采用分水岭分割算法把扩展后的LL分量图像分割成许多封闭而不重叠的小区域(空域分割),进行时空融合.可以在NLPR步态数据库中进行实验,结果表明,算法能够精确地识别完整的人体区域,拥有良好的抗噪性和适应性,进一步提高识别率.     

用行人轮廓的分布直方图分类和识别步态

现有的步态识别方法对行人轮廓匹配的鲁棒性差,识别率不高.提出了一种基于轮廓直方图分布的行人步态识别方法.首先提取行人二值轮廓序列;然后通过人体局部轮廓的点分布直方图获取步态周期;继而构造表达帧间关系的周期步态平面,设计一种直方图分布的描述子获得帧姿态特征值,计算出姿态轮廓特征分布间的Jeffery距离,结合动态时间规整技术获取了测试序列和参考序列间的匹配相似度,最终完成了识别.在Soton步态数据库上进行了实验,提出算法的正确识别率可达87.59%,与相关文献的时比分析表明算法是有效的.     

基于下肢轮廓特征的步态周期算法

步态周期是步态的一个重要特征,步态识别是建立在准确的步态周期分割之上的。本文提出了一个基于下肢轮廓的步态周期检测方法,首先对步态序列图像进行灰度化,然后计算各像素点在步态图像序列中的中值,获取整个步态序列图像的背景,提取人体目标后,利用数学形态学方法和区域跟踪算法填补二值化图像中的空洞;采用轮廓跟踪算法获得人体下肢轮廓,并将其转换为对应距离向量,在一个步态系列中利用距离向量范数研究步态周期。本算法计复杂度低,鲁棒性好,精确度高。     

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。     

用轮廓的点分布特征分析和识别步态

提出了一种轮廓点分布特征匹配方法来分析和识别行人步态。首先通过设计一种轮廓采样点区域分布直方图分析了步态的周期性特征,提取了一个步态周期的帧图像轮廓;继而采用一种局部轮廓描述子得到帧图像轮廓的点分布直方图阵列作为轮廓特征、用轮廓点集间的Hausdorff距离结合动态时间规整技术求取测试序列和参考序列间的匹配相似度;最后通过分类实现了人体步态识别。在Soton步态数据库进行了实验,正确分类识别率最高达到90.27%。相关文献的对比分析表明：该方法的识别率有较大的提高,是有效的。     

基于视频的正面步态周期检测方法

步态周期是步态的一个重要特征。本文提出了一个新的步态周期检测方法,首先对视频中的单帧图像进行灰度变换,然后计算各像素点在逐帧中的中值,以此作为整个序列图像的背景,用背景减除法提取人体目标,利用数学形态学方法和区域跟踪算法填补二值图像中的空洞,然后利用下肢摆动的面积变化来计算步态周期。本方法计算量小,精确度高,抗干扰性强,对步态的周期检测十分有效。     

基于视频的步态特征提取技术及其应用

提出一种新的基于视频的步态特征提取技术。首先对从视频流中抽取出帧图像,通过对二值化后的运动帧图像进行尺寸归一化和重心归一化。其次,根据归一化图像的宽高比的周期特性,提取出步态关键帧。最后,根据帧图像构建应用于不同识别算法的步态特征。实验结果表明,该技术简单、直观,具有较好完备性,能够直接应用于基于图像HMM和SVM步态识别算法。     

基于双特征匹配层融合的步态识别方法

步态识别是根据人类走路的姿态来进行远距离的身份识别。针对轮廓不完整的图 像和关键帧容易造成部分信息丢失而引起的识别率下降问题,提出一种基于双特征匹配层融合 的步态识别方法。步态既有静态图像特征,又有动态速度变化特征,因此本文提出用匹配层融 合方法将静态的 Hu 矩 6 个不变矩特征和动态的帧差百分比特征融合后进行步态身份识别。首 先对一个周期内的归一化步态图像进行 Hu 矩特征以及帧差百分比的特征提取,将 Hu 矩 6 个不 变矩特征描述成一个特征向量,然后运用匹配层融合算法对 2 个特征进行融合;最后使用 K 近 邻分类器进行身份识别。实验表明,该方法较单一方法能够有效地提高步态识别正确率。     

基于帧差时空特征的步态周期检测方法

针对传统基于可穿戴传感设备步态周期检测方法需要用户高度配合的问题,本文采用计算机视觉的方法来研究高精度步态周期检测算法。受帧差法的启示,本文设计一种全新的步态图像表达特征 帧差步态时空特征,该特征不仅蕴含了步态 运动的空间,还含有步态运动的时间信息,能较好地表达步态运动周期中的各个状态。基于帧差步态时空特征,设计足趾离地状态检测算法,进而实现步态周期的自动检测。实验结果表明,本方法能精确检测步态周期中的足趾离地状态帧。     

A framework for online gait recognition based on multilinear tensor analysis

The gait recognition is to recognize an individual based on the characteristics extracted from the gait image sequence. There are many researches for the gait recognition which use diverse kinds of information such as shape of gait silhouette, motion variation caused by walking, and so on. In general, shape information is more useful for recognition. However, shape information is influenced by a variety of factors, which degrade the recognition performance. Moreover, the information used in most of those studies might be able to be extracted after all of one or more sequences of the gait cycle are known. And it is also hard to discriminate the gait cycle from given gait sequences exactly by the online approach. In regard to these difficulties, we propose a novel gait recognition method based on the multilinear tensor analysis. To recognize the cyclic characteristic of gait without an exact division for the gait cycle, this paper’s propose is the method to form the accumulated silhouette and then describes those as the tensor. For the accumulated silhouette proposed by this paper, the image sequence of one gait cycle is divided into four sections in the training phase. However, discrimination for the gait cycle in the training phase is not directly related to the recognition phase, thus the online approach is possible. We first form the accumulated silhouettes for every individual using gait silhouettes within each section. And then, we represent these accumulated silhouettes as the tensor. Using a multilinear tensor analysis, we compute the core tensor which governs the interaction between factors organizing the original tensor, and then compose the basis to recognize the individual in the online recognition framework. Finally, we recognize the individual using the computation of similarity based on the Euclidean distance, which is more suitable to our method. We verify the superiority of the proposed approach via experiments with real gait sequences.     

Discriminant locally linear embedding with high-order tensor data.

Graph-embedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this framework, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local geometric properties within each class are preserved according to the locally linear embedding (LLE) criterion, and the separability between different classes is enforced by maximizing margins between point pairs on different classes. To deal with the out-of-sample problem in visual recognition with vector input, the linear version of DLLE, i.e., linearization of DLLE (DLLE/L), is directly proposed through the graph-embedding framework. Moreover, we propose its multilinear version, i.e., tensorization of DLLE, for the out-of-sample problem with high-order tensor input. Based on DLLE, a procedure for gait recognition is described. We conduct comprehensive experiments on both gait and face recognition, and observe that: 1) DLLE along its linearization and tensorization outperforms the related versions of linear discriminant analysis, and DLLE/L demonstrates greater effectiveness than the linearization of LLE; 2) algorithms based on tensor representations are generally superior to linear algorithms when dealing with intrinsically high-order data; and 3) for human gait recognition, DLLE/L generally obtains higher accuracy than state-of-the-art gait recognition algorithms on the standard University of South Florida gait database.     

基于迭代切距离原型学习算法的步态识别

作为唯一远程生物认证技术,步态识别一方面越来越受到人们的重视,提出了很多相应的算法,另一方面,它又面临着很多挑战,其难点之一是如何从多帧步态中有效地提取步态特征,针对此问题,并基于步态能量图(GEI)在步态特征表示上的效果,提出了一种迭代切距离原型学习算法,假定各人的步态分布在不同流形上面,首先用切距离改进步态能量图的定义,进而用迭代的方法来解一个最优解问题,从而学习出步态原型图,再通过PCA对步态原形进行特征提取,最后进行识别,证明了该方法的收敛性,实验结果表明所提出的方法取得了比GEl更好的识别率,并证明了步态流形的假设的合理性.     

基于傅立叶描述子的步态识别

提出了基于傅立叶描述子的步识别方法。用背景差方法得到运动人体的轮廊，通过步态周期分析提取步态序列的关键帧。利用傅立叶描述子处理关键帧的轮廊线序列，并进行数据维数压缩，得到匹配模板。用最近邻近法进行分类和识别。应用上述方法在Soton步态数据库上进行了实验，结果表明所提的步态识别方法具有罗高的识别性能。     

视频下的正面人体身份自动识别

为了能够实现视频下正面人体身份的自动识别,设计的系统包括Adaboost行人检测、Adaboost人脸检测、肤色验证、步态预处理、周期检测、特征提取以及决策级融合识别等模块.通过行人检测模块可以自动开启人脸检测模块和步态周期检测模块.实验结果表明,提出的根据下臂摇摆区域确定步态周期的方法对正面步态周期检测准确,计算量小,适用于实时的步态识别.采用人脸特征辅助步态特征在决策级的融合方法是解决视频下身份识别的新思路,在单样本的步态识别中,融合人脸特征可以提高识别精度.     

General tensor discriminant analysis and gabor features for gait recognition

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not.We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.     

MPCA: Multilinear Principal Component Analysis of Tensor Objects

This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2-D/3-D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2-D variant (2-D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA's utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.     

基于核主成分分析的步态识别方法

为了从多帧步态序列中更有效地提取步态特征并实时性地进行身份识别,提出一种有效的基于平均步态能量图(MGEI)的核主成分分析(KPCA)的身份识别方法。通过预处理技术提取出运动人体的侧面轮廓,根据步态下肢的摆动距离统计出步态周期,得到MGEI。KPCA采用非线性方法提取主成分,描述待识别图像中多个像素之间的相关性。利用KPCA的方法在高维空间对MGEI提取特征,选择合适的核函数,用方差倒数加权欧氏距离进行身份识别。实验结果表明,该算法具有较好的识别性能,并且耗时大大缩短。     

基于嵌入式隐马尔可夫模型的步态识别

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．