Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanlD Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.     

A robust similarity based deep siamese convolutional neural network for gait recognition across views

Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies.     

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。     

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．     

基于Tsfresh-RF特征提取的人体步态识别算法

惯性传感器(IMU)由于尺寸小、价格低、精度高以及信息实时性强等优点, 在人体运动信息的获取与控制等方面得到广泛应用, 但在步态识别的时间序列特征提取和步态环境数据等方面还存在着明显的局限. 本文针对人体下肢步态识别特征提取的复杂性及适用性差等问题, 提出基于Tsfresh-RF特征提取的人体步态识别新方法. 首先, 利用IMU获取的人体步态数据集, 构建基于Tsfresh时间序列特征提取和随机森林(RF)的人体步态识别算法模型. 其次, 采用该算法对人体不同传感器位置进行实验, 完成爬梯、行走、转弯等9种人体运动步态的识别. 最后, 实验结果表明所提方法平均分类准确率达到91.0％, 显著高于传统的支持向量机(SVM)与朴素贝叶斯(NB)等方法的识别结果. 此外, 本文所提基于Tsfresh-RF特征提取的人体步态识别算法具有很好的鲁棒性, 将为后续下肢外骨骼机器人的控制提供有利依据.     

基于角度直方图的步态识别算法

本文提出了一种简单实用的步态识别算法.该算法使用背景减除的方法检测人的运动区域;然后统计运动区域上像素的角度直方图,提取角度直方图向量作为步态特征;以欧氏距离作为度量,使用标准模式分类器用于步态识别.实验结果表明,本文提出的算法识别性能较高,并具有计算代价小等优点.     

基于VR设备中IMU的无手交互方法研究

随着头戴式显示设备的发展,在基于虚拟现实(VR,virtual reality)的教育培训中,存在用户与设备间进行交互的场景;针对用户与VR视频中对象的模拟靠近与躲避问题,提出了姿态感知与步态识别相结合的方法;通过姿态感知算法解算用户头部姿态,通过步态识别算法识别出人体的静止与行走状态,进而在行走状态时,将计算所得航向角和固定步长代入三角函数公式进行位置更新,在静止状态时,保持位置不变;实验证明,提出的姿态感知算法可以有效的计算出使用者头部的姿态,与商用惯性测量单元提供的姿态角相比具有1.1×10-2的平均姿态偏差;提出的步态识别算法可以有效地识别出人体的静止与行走状态;所提出的两者结合的交互方法,可以有效地实现虚拟的靠近与躲避.     

基于足底压力与支持向量机的步态相位识别研究

随着两足机器人、人工假肢技术以及为行走困难病人康复设计的康复训练机器人的发展,在线的步态相位识别方法越来越重要。本文提出的基于足底压力与支持向量机(SVM)的步态相位识别算法主要由五部分组成,即数据采集、数据预处理、特征提取、训练分类器和分类识别。实验表明：该方法能够对运动中的步态相位进行准确的判断。     

基于连续隐马尔可夫模型的步态识别

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.算法利用步态轮廓图像边界到重心的距离矢量对步态轮廓图像进行描述,采用步态图像的高宽比进行步态的准周期性分析.利用隐马尔可夫模型进行步态时变数据匹配识别.算法在CMU数据库上进行实验取得了较高的正确识别率.     

一种步态识别方法

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.算法利用步态轮廓图像边界到重心的距离矢量对步态轮廓图像进行描述,采用步态图像的高宽比进行步态的准周期性分析.利用隐马尔可夫模型进行步态时变数据匹配识别.算法在CMU数据库上面进行实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。     

基于约束满足的多目标双足机器人动态步态参数优化*

目前的步态优化算法仅仅实现了对单一目标的优化,把双足机器人步态优化看做是多目标优化问题,构建了衡量稳定性、能量消耗、步行速度三个目标评价函数。考虑到直接对多个目标加权求和的方法不能很好地处理多目标问题,提出一种新的基于约束满足的多目标步态参数优化算法,其思想是把基于惩罚函数的SPEA2(strength Pareto evolutionary algorithm2 )应用到多目标双足机器人动态步态参数优化问题上,规划出了同时满足这三个目标的动态优化步态。通过仿真实验表明了算法的有效性。     

Dynamic multiobjective optimization problems: test cases, approximations, and applications

After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm. Since in a dynamic multiobjective optimization problem, the resulting Pareto-optimal set is expected to change with time (or, iteration of the optimization process), a suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented. Moreover, a simple example of a dynamic multiobjective optimization problem arising from a dynamic control loop is presented. An extension to a previously proposed direction-based search method is proposed for solving such problems and tested on the proposed test problems. The test problems introduced in this paper should encourage researchers interested in multiobjective optimization and dynamic optimization problems to develop more efficient algorithms in the near future.     

用块稀疏贝叶斯学习算法重构识别体域网步态模式

针对低功耗体域网步态远程监测终端非稀疏加速度数据重构和步态模式识别性能优化问题,提出了一种基于块稀疏贝叶斯学习的体域网远程步态模式重构识别新方法,该方法基于体域网远程步态监测系统架构和压缩感知框架,在体域网传感节点利用线性稀疏矩阵压缩原始加速度数据,减少传输数据量,降低其功耗,同时在远程终端基于块稀疏贝叶斯学习算法充分利用加速度数据块结构内在相关性,获取加速度数据内在稀疏性,有效提高非稀疏加速度数据重构性能,为准确识别步态模式提供可靠的数据支撑.采用USC-HAD数据库中行走、跑、跳、上楼、下楼五种步态运动的加速度数据验证新方法的有效性,实验结果表明,基于所提算法的加速度数据重构性能明显优于传统压缩感知重构算法性能,使基于支持向量机多步态分类器识别准确率可达98%,显著提高体域网远程步态模式识别性能.所提新方法不仅有效提高非稀疏加速度数据重构和步态模式识别性能,并且也有助于设计低功耗、低成本的体域网加速度数据采集系统,为体域网远程监测步态模式变化提供一个新方法和新思路.     

GA-BP神经网络在下肢运动步态识别中的应用研究

为了提高下肢表面肌电信号步态识别的准确性,提出了一种基于遗传算法（GA）优化的BP神经网络分类器设计方法。首先,对采集的下肢表面肌电信号进行小波滤波及特征值提取,其次,构造基于GA优化的BP神经网络分类器,然后,以提取的表面肌电信号特征作为输入对分类器进行训练,最后利用训练好的分类器进行测试。实验结果表明,基于GA优化的BP神经网络分类器能成功识别下肢正常行走的五个步态,平均识别率达到98%以上,效果明显优于BP神经网络分类器的识别效果。     

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.     

一种新的步态图像序列分割算法

在运动目标步态识别中，从步态图像序列中提取出完整的人体运动轮廓对特征提取、目标分类和目标识别等有着非常重要的意义。提出了一种新的运动目标分割算法：首先应用改进的块匹配算法进行运动估计；然后运用分水岭算法把当前帧图像分割成许多封闭而不重叠的小区域；最后运用仿射参数模型进行运动块区域合并。在CMU步态数据库中采用基准算法进行的实验表明，运用所提出的算法能够提取出完整的人体轮廓，进一步提高步态识别的识别率。     

步态识别的深度学习:综述

由于步态容易受到物体遮挡、衣着、视角和携带物等协变量因素的影响,步态识别方法较难获得较优的识别性能.基于端到端和多层特征提取的思想,深度学习近年在步态识别领域取得一系列进展.本文综述深度学习在步态识别中的研究现状、优势和不足,总结其中的关键技术和潜在的研究方向.     

A new gait recognition system based on hierarchical fair competition-based parallel genetic algorithm and selective neural network ensemble

The recognition of a person from his or her gait has been a recent focus in computer vision because of its unique advantages such as being non-invasive and human friendly. However, gait recognition is not as reliable an identifier as other biometrics. In this paper, we applied a hierarchical fair competition-based parallel genetic algorithm and a neural network ensemble to the gait recognition problem. A diverse set of potential neural networks are generated to increase the reliability of the gait recognition, not only the best ones. Furthermore, a set of component neural networks is selected to build a gait recognition system such that generalization errors are minimized and negative correlation is maximized. Experiments are carried out with the NLPR and SOTON gait databases and the effectiveness of the proposed method for gait recognition is demonstrated and compared to previous methods.