基于加速度传感器的可扩展手势识别

为了提高基于加速度传感器的动态手势识别算法的性能,并且增强系统的可扩展性,提出了一种有效结合机器学习模型与模板匹配的方法.将手势分为基本手势和复杂手势两大类,其中复杂手势可分割为基本手势组成的序列;根据手势运动的特点提取有效的特征量,并利用基本手势样本训练随机森林模型,然后用其对基本手势序列进行分类预测;将预测结果进行约翰逊编码,再与标准模板序列进行相似度匹配.实验结果表明,该方法获得了99.75%的基本手势识别率以及100%的复杂手势识别率.算法既保证了手势识别的精度,也提高了系统的可扩展性.     

基于指尖角度集核密度估计手势特征提取

针对手势识别实时性和鲁棒性不足的问题,提出基于指尖角度集核密度估计的特征提取方法。通过对一般手势定义指尖角度集并提取指尖角度集核密度估计特征。为解决形状匹配的相位漂移问题和进一步提高实时性,对该特征集有效区间归一化和均匀采样得到指尖角度集核密度估计序列。基于互相关系数形状匹配算法进行手势识别。实验分析表明,采用该特征提取方法的任意手势识别实时性和鲁棒性比现有方法有显著提高。     

手势认证中基于能量熵的端点检测方法研究

为了保证智能手机敏感信息的安全性,设计实现了一种基于手机内置三轴加速度传感器的三维手势认证方案。在手势端点检测部分,在定性分析手势加速度信号能量分布特性的基础上,提出了一种基于能量熵的新方法实现有效手势截取。进一步设计基于欧式距离的动态时间规整算法对截取后的手势序列信号进行匹配认证,当他人模仿手势错误接受率趋近0%时,本人认证手势错误拒绝率维持在7%左右,从而实现智能手机用户身份识别。     

一种信道状态信息下的复杂动态手势识别方法

传统动态手势识别方法中存在硬件成本高,推广难度大等局限性.文章提出一种基于信道状态信息的复杂动态手势识别方法CSI-Num,该方法可用来实现对空中数字手势的高效识别.CSI-Num识别过程主要分为两个阶段:数据提取处理阶段与手势匹配识别阶段.提取处理阶段,是将采集到的数据,选取能够反映手势动作的子载波特征值作为被选信号,通过小波阈值函数和五点三次平滑方法对信号进行降噪平滑;匹配识别阶段,提取有效手势数据,使用k均值聚类算法和动态时间规整算法特性相融合的K-DTW匹配算法识别出不同数字的手势动作.实验结果表明,针对不同环境的室内场景,相应地调整参数设置,CSI-Num可以高效地识别出不同数字的手势动作,且具有较高鲁棒性.     

基于加速度传感器的大手势集手势识别算法改进研究

搭载着加速度传感器的智能移动终端为手势识别提供广泛的应用平台,在已有的基于单枚加速度传感器的手势识别研究中,识别正确率、速度和手势集合难以达到良好的平衡。本文以一种基于加速度符号序列的识别算法为基础,改进了其特征提取方法,并设计了基于加权树结构模板库的匹配方法,实现了大手势集、高正确率、速度快的手势识别。实验表明,所述系统在21个手势组成的手势集合上,实现了95.2%的用户依赖识别率和94.6%的用户非依赖识别率,识别时间小于10毫秒,对手势识别研究有一定的借鉴价值。     

应用交叉覆盖算法实现静态手势识别

静态手势识别是实现人机交互的前提和基础,多数手势识别采用模板匹配或人工神经网络算法。基于交叉覆盖算法的手势识别,首先通过摄像头采集到的手势图像经过灰度变换、平滑、二值化等预处理,在训练阶段,用交叉覆盖算法对二值化手势图像进行训练以得到手势分类,最后在测试集上进行手势识别。实验结果表明,由于该算法避免了优化过程中所需的巨大运算量,且允许分类目标在一定范围内的动态变化,手势识别的准确率得到有效提高。     

基于轨迹模板匹配的动态手势识别方法

首先采用基于混合高斯模型与椭圆肤色模型进行手势分割,分割出手势区域,使用卡尔曼滤波器进行手势跟踪,获得手势中心点的位置.在此基础上,记录各帧中心点位置,得到运动轨迹,利用提出的轨迹模板匹配方法对动态手势进行识别.该方法利用基本的几何特征便可完成手势运动轨迹的设置与识别,无需特征选择或训练样本的搜集.最后,采用基于Zynq-7000的Zedboard平台对该算法进行实现,并采用HLS硬件加速工具进行算法加速.实验结果表明,该算法可实现较精确的手势识别,接受弹性的输入采样,识别正确率在95%以上,且通过硬件加速后,可在嵌入式平台中实时识别,具有较好的实时性.     

基于几何特征的静态字母手势识别算法

针对聋哑人哑语手势自动识别问题的复杂性,研究了手势几何特征的多样性及提取和识别方法,提出了一种基于几何特征的手势识别算法.首先,对手势图像进行肤色分割、边缘检测以及逻辑运算,然后,计算其质心面积等多项几何特征,通过实验方法测定最佳特征权值,最后,将其与样本图像特征值进行匹配,最佳匹配即为检测结果.根据30个字母手势创建了3套手势库,其中1套作为样本集,2套作为测试集.实验结果表明,通过该方法进行特征提取来识别汉语字母手势,可有效提高识别率,测试集识别率达到93.33％.     

基于SURF特征跟踪的动态手势识别算法

提出了一种基于加速鲁棒特征(SURF)跟踪的动态手势识别算法.其特征在于算法无需预先检测分割人手区域,仅通过跟踪统计相邻帧间匹配SURF特征点的移动主方向来刻画手势运动轨迹.提出采用经时间规整的轨迹方向数据流来建立动态手势模型,利用基于相关分析的数据流聚类方法实现动态手势的识别,大大提高动态手势识别速度.实验使用26个英文字母作为动态手势训练和识别,手势训练集和测试集的识别率分别为87.1%和84.6%,并成功用于实验室自主研制的侦察移动机器人Hunter的运动控制中,证实了该方法的有效性.     

基于弹性图匹配的静态手势识别

手势识别已成为人机交互技术的关键之一.本文提出了基于弹性图匹配的静态手势识别算法.弹性图匹配的关键是Gabor函数参数的选择,本文通过分析Gabor函数特性,恰当地选择了Gabor函数的参数,并考虑手势的平移、旋转和大小,对1到9的数字手势进行了识别实验,结果表明本算法是可行的.     

A high-performance training-free approach for hand gesture recognition with accelerometer

In previous research on human machine interaction, parameters or templates of gestures are always learnt from training samples first and then a certain kind of matching is conducted. For these training-required methods, a small number of training samples always result in poor or user-independent performance, while a large quantity of training samples lead to time-consuming and laborious sample collection processes. In this paper, a high-performance training-free approach for hand gesture recognition with accelerometer is proposed. First, we determine the underlining space for gesture generation with the physical meaning of acceleration direction. Then, the template of each gesture in the underlining space can be generated from the gesture trails, which are frequently provided in the instructions of gesture recognition devices. Thus, during the gesture template generation process, the algorithm does not require training samples any more and fulfills training-free gesture recognition. After that, a feature extraction method, which transforms the original acceleration sequence into a sequence of more user-invariant features in the underlining space, and a more robust template matching method, which is based on dynamic programming, are presented to finish the gesture recognition process and enhance the system performance. Our algorithm is tested in a 28-user experiment with 2,240 gesture samples and this training-free algorithm shows better performance than the traditional training-required algorithms of Hidden Markov Model (HMM) and Dynamic Time Warping (DTW).     

基于Kinect手势识别的应用与研究

为解决当前智能家居系统操作繁琐的问题,同时为获得更简单的控制方式,并增加用户的体验感受,研究了基于Kinect骨骼信息的手势识别技术,并将其融入至智能家居的人机交互系统中。在该系统中,用户可以自定义手势动作或语音实现家居设备的智能控制。使用了一种基于加权动态时间规整的模板匹配手势识别算法。通过Kinect的深度摄像头获取手势深度图像和骨骼图像数据,并采用加权动态时间规整算法进行识别。实验表明使用该算法实现手势识别是可行且有效的,且其最佳识别位置是在Kinect的正前方2～2.5m处,识别准确率达到96%左右。     

一种基于手机加速度传感器的三维手势身份认证方法

针对手机用户安全问题,提出一种基于手机加速度传感器的手势身份认证方法。采用均值—方差归一化方式对三维手势数据进行归一化处理;采用门限值方法截取手势动作,去除干扰数据;认证算法采用模板匹配的方式,通过设计的均值—动态时间归整(A-DTW)算法对参考模板和测试模板进行比较,判断用户的真实性。仿真结果显示:该算法方便可行,具有较高的识别率。     

A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL).     

Coupled grouping and matching for sign and gesture recognition

Matching an image sequence to a model is a core problem in gesture or sign recognition. In this paper, we consider such a matching problem, without requiring a perfect segmentation of the scene. Instead of requiring that low- and mid-level processes produce near-perfect segmentation, we take into account that such processes can only produce uncertain information and use an intermediate grouping module to generate multiple candidates. From the set of low-level image primitives, such as constant color region patches found in each image, a ranked set of salient, overlapping, groups of these primitives are formed, based on low-level cues such as region shape, proximity, or color. These groups corresponds to underlying object parts of interest, such as the hands. The sequence of these frame-wise group hypotheses are then matched to a model by casting it into a minimization problem. We show the coupling of these hypotheses with both non-statistical matching (match to sample-based modeling of signs) and statistical matching (match to HMM models) are possible. Our algorithm not only produces a matching score, but also selects the best group in each image frame, i.e. recognition and final segmentation of the scene are coupled. In addition, there is no need for tracking of features across sequences, which is known to be a hard task. We demonstrate our method using data from sign language recognition and gesture recognition, we compare our results with the ground truth hand groups, and achieved less than 5% performance loss for both two models. We also tested our algorithm on a sports video dataset that has moving background.     

利用轨迹模板匹配方法的实时动态手势识别

利用混合高斯模型进行运动检测,分割出运动前景,采用粒子滤波器结合皮肤椭圆模型进行手势跟踪,获得手势中心点运动轨迹,在此基础上提出利用轨迹模板匹配方法进行动态手势识别.该方法利用基本的几何和三角函数就能完成手势运动轨迹的定义和识别,不需要选择特征或训练样本.实验结果表明,该算法能够实现实时动态手势识别.