Silhouette-based gait recognition via deterministic learning

In this paper, we present a new silhouette-based gait recognition method via deterministic learning theory, which combines spatio-temporal motion characteristics and physical parameters of a human subject by analyzing shape parameters of the subject?s silhouette contour. It has been validated only in sequences with lateral view, recorded in laboratory conditions. The ratio of the silhouette?s height and width (H–W ratio), the width of the outer contour of the binarized silhouette, the silhouette area and the vertical coordinate of centroid of the outer contour are combined as gait features for recognition. They represent the dynamics of gait motion and can more effectively reflect the tiny variance between different gait patterns. The gait recognition approach consists of two phases: a training phase and a test phase. In the training phase, the gait dynamics underlying different individuals? gaits are locally accurately approximated by radial basis function (RBF) networks via deterministic learning theory. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the test phase, a bank of dynamical estimators is constructed for all the training gait patterns. The constant RBF networks obtained from the training phase are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L₁ norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, the recognition performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches on the most well-known public gait databases: CASIA, CMU MoBo and TUM GAID.     

傅立叶描述子识别物体的形状

傅立叶描述子是分析和识别物体形状的重要方法之一，利用基于曲线多边形近似的连续傅立叶变换方法计算傅立叶描述子，并通过形状的主方向消除边界起始点相位影响的方法，定义了新的具有旋转，平移和尺度不变性的归一化傅立叶描述子，与使用离散傅立叶变换和模归一化的传统傅立叶描述了相比，新的归一化傅立叶描述于同时保留了模与相位特性，因此能够更好地识别物体的形状，实验表明这种新的归一化傅立叶描述子比传统的傅立叶描述子能够更加高效，准确地识别物体的形状。     

多特征和多视角信息融合的步态识别

提出了一种基于多特征和多视角信息融合的步态识别方法。应用背景差分和阴影消除获得人体步态轮廓,对人体轮廓使用伪Zernike矩、小波描述子和Procrustes形状分析法进行了特征提取。通过多特征和多视角步态信息融合,完成了基于人体步态特征的身份识别。该方法在CASIA步态数据库上进行了实验,取得了较高的正确识别率,实验结果表明本文所提出的识别方法具有较高的识别性能。     

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.     

基于图像背景减除技术的步态识别研究

步态识别是一种新的生物特征识别技术,旨在根据人们走路的姿势进行身份识别。本文利用图像背景减除技术进行步态轮廓检测,然后利用傅立叶描述子对步态轮廓图像进行描述,进行维数压缩,得到模板匹配,最后,利用最邻近法进行识别。实验证明,该算法具有较高的识别率。     

人体行为动作的形状轮廓特征提取及识别

将傅里叶变换与边缘小波矩描述子相结合,提出一种人体行为动作的识别方法。凹凸复杂图像的质心到轮廓为非单一直线,据此,给出一种多段定向距离轮廓描述矩阵,实现轮廓特征的提取。分别对2类人体和4种行为动作进行仿真实验,结果表明,边缘小波矩描述子能较好地体现人体行为动作的形状轮廓局部特征,具有较高的识别率。     

基于图像背景减除技术的步态识别研究

步态识别是一种新的生物特征识别技术,旨在根据人们走路的姿势进行身份识别。本文利用图像背景减除技术进行步态轮廓检测,然后利用傅立叶描述子对步态轮廓图像进行描述,进行维数压缩,得到模板匹配,最后,利用最邻近法进行识别。实验证明,该算法具有较高的识别率。     

利用多源运动信息的下肢假肢多模式多步态识别研究

运动状态识别对智能下肢假肢的控制非常关键,本文利用下肢表面肌电信号、腿部角度和足底压力信号在运动模式和步态分析中的优势和特点,对下肢假肢的多模式多步态识别进行研究.通过建立下肢运动信息系统,获取下肢多源运动信息.先提取下肢肌电信号的小波包能量作为特征,建立多个HMM对下肢假肢的运动模式进行识别;再根据大小腿和膝关节的角...     

基于傅里叶变换的形状上下文描述方法

提出了一种新的基于傅里叶变换的形状上下文描述方法,与以前的形状上下文描述子相比,增加了天然的旋转不变性,同时描述子本身也更加简洁.该傅里叶形状描述子,将全局采样点的分布信息有机地结合到了每个采样点的特征描述中去,从而使代表不同形状的采样点集可以得到准确的鉴别匹配.进一步考虑点集之间仿射变换的代价,从而可以很好地运用到二维形状分类识别问题中去.该方法满足平移、缩放、旋转三个不变性,在形状有遮挡、缺损的情况下都可以取得较好的容错识别效果.     

A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification

Re-identification refers to the problem of establishing correspondence among various observations of the same subject viewed at different time instances in different camera positions. We propose a hierarchical approach for re-identifying a subject by combining gait with phase of motion and a spatiotemporal model. The fundamental nature of the gait biometric of being amenable to capturing from a distance even at low resolution without active co-operation of subjects, has motivated us to use it for re-identification. We use two features related to a subject’s motion dynamics, one is his exit/entry phase of motion and the other is his gait signature. An additional third feature is obtained from the spatiotemporal model of the camera network which is learnt during the training phase in the form of a multivariate probability density of space–time variables (entry/exit location, exit velocity, and inter-camera travel time) using kernel density estimation. Once all these three features have been computed, correspondences are established by dynamic programing based maximum likelihood (ML) estimation. The performance of our method has been evaluated on a real data set featuring a two-camera and a three-camera network in a hallway monitoring situation. The proposed approach shows promising results on both the data sets.     

A model-based hand gesture recognition system

This paper introduces a model-based hand gesture recognition system, which consists of three phases: feature extraction, training, and recognition. In the feature extraction phase, a hybrid technique combines the spatial (edge) and the temporal (motion) information of each frame to extract the feature images. Then, in the training phase, we use the principal component analysis (PCA) to characterize spatial shape variations and the hidden Markov models (HMM) to describe the temporal shape variations. A modified Hausdorff distance measurement is also applied to measure the similarity between the feature images and the pre-stored PCA models. The similarity measures are referred to as the possible observations for each frame. Finally, in recognition phase, with the pre-trained PCA models and HMM, we can generate the observation patterns from the input sequences, and then apply the Viterbi algorithm to identify the gesture. In the experiments, we prove that our method can recognize 18 different continuous gestures effectively. Received: 19 May 1999 / Accepted: 4 September 2000     

复杂背景下基于傅立叶描述子的手势识别

人的手势是人们日常生活中最广泛使用的一种交流方式。由于在人机交互界面和虚拟现实环境中的应用,手势识别的研究受到了越来越广泛的关注。但是目前基于单目视觉的手势识别技术中,手势分割要求背景简单或者要求识别者戴着笨重的数据手套。而该文结合了运动信息和基于KL变换的肤色模型,在复杂背景下进行手势分割,与传统的基于RGB肤色模型的手势分割相比,在复杂背景环境下得到了很好的分割效果。在对分割的手势区域进行预处理后,该文使用了一种归一化的傅立叶描述子进行手势的特征提取,相比传统的傅立叶描述子更加准确,最后采用了传统的三层BP网络作为模式识别器,手势训练集和测试集的识别率分别达到了95．9％和95％。