Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.     

Statistical motion model based on the change of feature relationships: human gait-based recognition

We offer a novel representation scheme for view-based motion analysis using just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or part-level tracking. We model the relational statistics using the probability that a random group of features in an image would exhibit a particular relation. To reduce the representational combinatorics of these relational distributions, we represent them in a Space of Probability Functions (SoPF), where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this space. We demonstrate and evaluate the effectiveness of this representation in the context of recognizing persons from gait. In particular, on outdoor sequences: (1) we demonstrate the possibility of recognizing persons from not only walking gait, but running and jogging gaits as well; (2) we study recognition robustness with respect to view-point variation; and (3) we benchmark the recognition performance on a database of 71 subjects walking on soft grass surface, where we achieve around 90 percent recognition rates in the presence of viewpoint variation.     

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.     

一种改进的基于地面反作用力的步态识别方法

提出一种改进的基于地面反作用力的步态识别方法．该方法通过由三维测力台构建的步态通道获取步行时足底受到的三方向地面反作用力,并采用小波包分解提取时频域特征,利用模糊C 均值聚类算法从中挑选出最具分类能力的特征子集,最后在训练样本上用支持向量机训练分类器,并在测试集上进行步态识别．为提高识别率,对样本进行拆分和波形对齐操作,并设计多分类器以降低步行速度变化对识别准确率的影响．在103人的步态数据库上的测试结果表明,该方法即使在训练样本较少的情况下也可以得到较高的识别率．     

Individual recognition using gait energy image

In this paper, we propose a new spatio-temporal gait representation, called gait energy image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.     

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。     

一种改进的步态识别方法

步态识别通过人体走路的姿势来识别人的身份。近年来,步态作为一种生物特征识别技术备受计算机视觉研究者的关注。对某个人的一个步态序列利用动态Viterbi算法得到一个样本姿态序列,对其多个步态样本姿态序列的对应姿态取平均得到这个人的特征姿态序列,对特征姿态采用主成分分析法和线性判别分析法处理特征空间,并用最近邻法进行识别。利用CASIA数据库对本文方法进行验证,取得了较高的识别率,并对体形变化具有较强的鲁棒性。     

一种基于隐马尔科夫模型的步态识别算法

步态是指人体走路时的姿态,步态识别是近年来生物特征识别领域一个备受关注的研究方向。步态阶段的区分是步态识别的重要内容。以隐马尔科夫模型(HMM)为基础,基于安装在膝关节的编码器和大腿部的加速度传感器,在外骨骼辅助行走中识别步态的不同阶段。首先进行数据预处理和特征提取;其次对隐马尔科夫步态识别算法进行设计,包括结构的建立、参数的训练和最终的识别;最后对性能进行评估,总体正确率达到91.06%,说明HMM用于步态阶段识别具有较好的性能。     

基于多区域分割的步态表示与识别算法研究

提出了一种基于多区域分割的步态表示方法,将视频中的目标人体区域部分按人体结构特点划分为多个子区,每个子区通过连接人体重心的椭圆进行拟和,建立多连接椭圆的人体结构模型,预处理阶段引入一个归一化过程以实现对各种视角下的步态序列进行表示.识别过程将模型参数作为步态特征,并引入了一定的融合机制对特征进行选择和融合.实验表明,该算法对于多个视角下的步态序列均具有较好的识别性能.     

View-invariant gait recognition based on kinect skeleton feature

Gait recognition is a popular remote biometric identification technology. Its robustness against view variation is one of the challenges in the field of gait recognition. In this paper, the second-generation Kinect (2G–Kinect) is used as a tool to build a 3D–skeleton-based gait dataset, which includes both 2D silhouette images captured by 2G–Kinect and their corresponding 3D coordinates of skeleton joints. Given this dataset, a human walking model is constructed. Referring to the walking model, the length of some specific skeletons is selected as the static features, and the angles of swing limbs as the dynamic features, which are verified to be view-invariant. In addition, the gait recognition abilities of the static and dynamic features are investigated respectively. Given the investigation, a view-invariant gait recognition scheme is proposed based on the matching-level-fusion of the static and dynamic features, and the nearest neighbor (NN) method is used for recognition. Comparison between the existing Kinect-based gait recognition method and the proposed one on different datasets show that the proposed one has better recognition performance.     

Silhouette analysis-based gait recognition for human identification

Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost.     

基于步态的身份识别

提出了一种简单有效的自动步态识别算法，对于每个序列而言，一种改进的背景减除方法用于检测行人的运动轮廓，然后，这些时变的2D轮廓形状被转换为对应的1D距离信号，同时通过特征空间变换来提取低维步态特征，基于时空相关或归一化欧氏距离度量，标准的模式分类技术用于最终的识别，实验结果表明该算法不仅获得了令人鼓舞的识别性能，而且拥有相对较低的计算代价。     

基于生成对抗网络的跨视角步态特征提取

步态是一种能够在远距离、非侵犯的条件下识别身份的生物特征,但在实际场景中,步态很容易受到拍摄视角、行走环境、物体遮挡、着装等因素的影响.在跨视角识别问题上,现有方法只注重将多种视角的步态模板转化到固定视角下,且视角跨度的增大加深了错误的累积.为了提取有效的步态特征用于跨视角步态识别,本文提出了一种基于生成对抗网络的跨视角步态特征提取方法,该方法只需训练一个模型即可将步态模板转换到任意视角下的正常行走状态,并最大化地保留原本的身份特征信息,从而提高步态识别的准确率.在CASIA-B和OUMVLP数据集上的实验结果表明,该方法在解决跨视角步态识别问题上具有一定的鲁棒性和可行性.     

Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics

Gait recognition algorithms often perform poorly because of low resolution video sequences, subjective human motion and challenging outdoor scenarios. Despite these challenges, gait recognition research is gaining momentum due to increasing demand and more possibilities for deployment by the surveillance industry. Therefore every research contribution which significantly improves this new biometric is a milestone. We propose a probabilistic sub-gait interpretation model to recognize gaits. A sub-gait is defined by us as part of the silhouette of a moving body. Binary silhouettes of gait video sequences form the basic input of our approach. A novel modular training scheme has been introduced in this research to efficiently learn subtle sub-gait characteristics from the gait domain. For a given gait sequence, we get useful information from the sub-gaits by identifying and exploiting intrinsic relationships using Bayesian networks. Finally, by incorporating efficient inference strategies, robust decisions are made for recognizing gaits. Our results show that the proposed model tackles well the uncertainties imposed by typical covariate factors and shows significant recognition performance.     

一种笔段序列匹配联机汉字识别方法

文中针对行书体汉字的识别,提出一种笔段序列匹配汉字识别方法。选择笔段数、笔段书写顺序、笔段位置作为主要的识别特征。首先,在预处理阶段,为了减小汉字类内分散性,采用笔段密度均衡化非线性规整法对笔段位置进行了非线性规整;进而在笔段序列的基础上,采用动态规划算法寻找待识笔段序列与候选笔段序列的最优匹配路径;根据此路径,得到匹配笔段集,缺少笔段和多余笔段;然后计算出待识字与每个候选字的识别距离;最后,对识     

姿态特征结合2维傅里叶变换的步态识别

目的 针对现有步态识别方法易受携带物品、衣服变化等影响的问题,提出了将无肩姿态能量图、步态参数等姿态特征与步态参数的2维傅里叶变换相结合的步态识别算法。方法 基于姿态关节点序列提出忽略肩膀宽度信息的无肩姿态能量图,用以减弱衣服变化的影响;由于下肢受衣物及背包影响较小,提取3个或3个以上的下肢关节点局部结构参数,即提取中臀点与左右膝关节点、中臀点与左右踝关节点构成的两个三角形面积以及所有下肢关节点构成的多边形面积作为步态参数,增强下肢参数在步态识别中的作用;人在行走时,单肢体的运动具有一定的周期性,且肢体之间运动具有一定的协调性,用步态参数的2维幅度谱来表示单肢体运动的周期性与肢体之间运动的协调性,以提高步态参数的可区别性;在现有典型步态特征的基础上,融合本文提出的无肩姿态能量图、步态参数及其2维傅里叶变换幅度谱,采用多特征表示步态的方法,充分利用各特征的优点,提出加权平均与最大池化相结合的两层分数融合策略进行步态识别,提高了步态识别算法在携带物品、衣服变化和跨视角等条件下的正确率。结果 实验结果表明,在中国科学院自动化研究所发布的步态数据集CASIA-B上,本文方法在相同视角条件下,正常状态、背包状态和穿大衣状态的平均识别率分别为99.56%、99.23%和94.25%;在跨视角条件下,正常状态、背包状态和穿大衣状态的平均识别率分别为91.32%、85.34%和69.51%。与典型算法相比,穿大衣状态的识别率有显著提升。结论 本文方法采用加权平均与最大池化相结合的两层分数融合策略,综合利用各特征的优点及其适用场景,有效提高了步态识别的准确率,特别是减弱了衣服厚度、样式等变化对步态识别的影响。     

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。     

共享转换矩阵的胶囊网络及其融合视角特征的跨视角步态识别

步态识别具有非接触性、非侵犯性、易感知等优势,然而,在跨视角的步态识别中,行人的轮廓会随人的视角的变化而不同,从而影响步态识别的性能。为此,提出了共享转换矩阵的胶囊网络及其改进的动态路由算法,从而减少了网络训练参数。在此基础上,通过融合视角特征,利用Triplet损失与Margin损失提出了融合视角特征的跨视角步态识别模型。在CASIA-B数据集上的实验结果表明,使用共享转换矩阵的胶囊网络提取步态特征是有效的,在正常行走、携带背包、穿戴外套条件下,所提融合视角特征的模型在识别准确率上比基于卷积神经网络的跨视角步态识别方法提高了4.13%,且对跨较大视角的步态识别具有更好的性能。     

基于流形学习的三维步态鲁棒识别方法

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。