基于Petri网和BPNN的多重触控手势识别

为解决多重触控技术的手势识别问题,提出一个多重触控手势描述与识别框架,给出其描述和识别方法。多重触控手势可分为原子手势和组合手势,在手势描述过程中,利用BP网络对原子手势进行建模,然后在将用户的意图映射为原子手势逻辑、时序和空间关系关联而成的组合手势,并在Petri网引入逻辑、时序和空间关系描述符对组合手势进行描述。在手势识别过程中,根据BP网络分类器检测出原子手势,并触发组合手势Petri网模型的转移,实现组合手势的识别。实验结果表明该方法对不同用户操作习惯有鲁棒性,能有效解决多重触控手势识别问题。     

基于Leap Motion的动态手势识别

动态手势识别作为人机交互的一个重要方向，在各个领域具有广泛的需求。相较于静态手势，动态手势的变化更为复杂，对其特征的充分提取与描述是准确识别动态手势的关键。为了解决对动态手势特征描述不充分的问题，利用高精度的Leap Motion传感器对手部三维坐标信息进行采集，提出了一种包含手指姿势和手掌位移的特征在内的、能够充分描述复杂动态手势的特征序列，并结合长短期记忆网络模型进行动态手势识别。实验结果表明，提出的方法在包含16种动态手势的数据集上的识别准确率为98.50%；与其他特征序列的对比实验表明，提出的特征序列，能更充分准确地描述动态手势特征。     

Real-time 3D pointing gesture recognition for mobile robots with cascade HMM and particle filter

In this paper, we present a real-time 3D pointing gesture recognition algorithm for mobile robots, based on a cascade hidden Markov model (HMM) and a particle filter. Among the various human gestures, the pointing gesture is very useful to human-robot interaction (HRI). In fact, it is highly intuitive, does not involve a-priori assumptions, and has no substitute in other modes of interaction. A major issue in pointing gesture recognition is the difficultly of accurate estimation of the pointing direction, caused by the difficulty of hand tracking and the unreliability of the direction estimation.The proposed method involves the use of a stereo camera and 3D particle filters for reliable hand tracking, and a cascade of two HMMs for a robust estimate of the pointing direction. When a subject enters the field of view of the camera, his or her face and two hands are located and tracked using particle filters. The first stage HMM takes the hand position estimate and maps it to a more accurate position by modeling the kinematic characteristics of finger pointing. The resulting 3D coordinates are used as input into the second stage HMM that discriminates pointing gestures from other types. Finally, the pointing direction is estimated for the pointing state.The proposed method can deal with both large and small pointing gestures. The experimental results show gesture recognition and target selection rates of better than 89% and 99% respectively, during human-robot interaction.     

融合关节旋转特征和指尖距离特征的手势识别

作为人机交互的重要方式,手势交互和识别由于其具有的高自由度而成为计算机图形学、虚拟现实与人机交互等领域的研究热点.传统直接提取手势轮廓或手部关节点位置信息的手势识别方法,其提取的特征通常难以准确表示手势之间的区别.针对手势识别中不同手势具有的高自由度以及由于手势图像分辨率低、背景杂乱、手被遮挡、手指形状尺寸不同、个体差异性导致手势特征表示不准确等问题,本文提出了一种新的融合关节旋转特征和指尖距离特征的手势特征表示与手势识别方法.首先从手势深度图中利用手部模板并将手部看成链段结构提取手部20个关节点的3D位置信息;然后利用手部关节点位置信息提取四元数关节旋转特征和指尖距离特征,该表示构成了手势特征的内在表示;最后利用一对一支持向量机对手势进行有效识别分类.本文不仅提出了一种新的手势特征表示与提取方法,该表示融合了关节旋转信息和指尖距离特征;而且从理论上证明了该特征表示能唯一地表征手势关节点的位置信息;同时提出了基于一对一SVM多分类策略进行手势分类与识别.对ASTAR静态手势深度图数据集中8类中国数字手势和21类美国字母手势数据集分别进行了实验验证,其分类识别准确率分别为99.71%和85.24%.实验结果表明,本文提出的基于关节旋转特征和指尖距离特征的融合特征能很好地表示不同手势的几何特征,能准确地表征静态手势并进行手势识别.     

基于加锁机制的静态手势识别方法

基于RGB-D（RGB-Depth）的静态手势识别的速度高于其动态手势识别,但是存在冗余手势和重复手势而导致识别准确性不高的问题。针对该问题,提出了一种基于加锁机制的静态手势识别方法来识别运动中的手势。首先,将通过Kinect设备获取RGB数据流和Depth数据流融合成人体骨骼数据流;然后,在静态手势方法中引入加锁机制,并与之前建立好的骨骼点特征模型手势库进行比对计算;最后,设计一款“程序员进阶之路”益智类网页游戏进行应用与实验。实验验证在6种不同运动手势情况下,该方法与纯静态手势识别方法相比,平均识别准确率提高了14.4%;与动态手势识别相比,识别速度提高了14%。实验结果表明,提出的基于加锁机制的静态手势识别方法,既保留了静态识别的速率,实现了实时识别;又能很好地剔除冗余手势和重复手势,提高了识别正确性。     

A database-based framework for gesture recognition

Gestures are an important modality for human–machine communication. Computer vision modules performing gesture recognition can be important components of intelligent homes, assistive environments, and human–computer interfaces. A key problem in recognizing gestures is that the appearance of a gesture can vary widely depending on variables such as the person performing the gesture, or the position and orientation of the camera. This paper presents a database-based approach for addressing this problem. The large variability in appearance among different examples of the same gesture is addressed by creating large gesture databases, that store enough exemplars from each gesture to capture the variability within that gesture. This database-based approach is applied to two gesture recognition problems: handshape categorization and motion-based recognition of American Sign Language signs. A key aspect of our approach is the use of database indexing methods, in order to address the challenge of searching large databases without violating the time constraints of an online interactive system, where system response times of over a few seconds are oftentimes considered unacceptable. Our experiments demonstrate the benefits of the proposed database-based framework, and the feasibility of integrating large gesture databases into online interacting systems.     

一种基于深度图像的静态手势神经网络识别方法

静态手势识别是以手势驱动的人机交互系统的核心技术。针对静态手势识别问题,提出了一种基于深度图像进行静态手势识别的方法。为了消除静态手势识别过程中的平移、旋转和缩放不变性,提取手势轮廓的Hu不变矩,并以Hu不变矩作为特征构建静态手势深度感知神经网络模型,以此实现对静态手势进行分类识别。在VisualStudio的开发环境下实现了对该方法的验证,取得了良好的效果,并与传统的模板匹配法与基于卷积神经网络的深度学习方法作比较,静态手势识别准确率总体可达95%,识别效率高,能满足实时性要求。     

Adaptive visual gesture recognition for human–robot interaction using a knowledge-based software platform

In human–human communication we can adapt or learn new gestures or new users using intelligence and contextual information. Achieving natural gesture-based interaction between humans and robots, the system should be adaptable to new users, gestures and robot behaviors. This paper presents an adaptive visual gesture recognition method for human–robot interaction using a knowledge-based software platform. The system is capable of recognizing users, static gestures comprised of the face and hand poses, and dynamic gestures of face in motion. The system learns new users, poses using multi-cluster approach, and combines computer vision and knowledge-based approaches in order to adapt to new users, gestures and robot behaviors. In the proposed method, a frame-based knowledge model is defined for the person-centric gesture interpretation and human–robot interaction. It is implemented using the frame-based Software Platform for Agent and Knowledge Management (SPAK). The effectiveness of this method has been demonstrated by an experimental human–robot interaction system using a humanoid robot ‘Robovie’.     

复杂背景下的手势分割与识别

目前在基于单目视觉的手势识别中,手势分割技术几乎都是基于简单的背景或者要求 手势者带有特殊颜色的手套,给人机交互增加了一定的限制.本文融合人手颜色信息和手势运 动信息,两次利用种子算法对复杂背景下的手势进行分割.根据分割出的手区域大大加速了运动 特征参数的提取,并结合手区域的形状特征,建立手势的时空表观模型.识别时,采用独立分布的 多状态高斯概率模型,进行时间规整.手势训练集和测试集的识别率分别为97.8%和95.6%.     

连续动态手势的时空表观建模及识别

论述了复杂背景下连接动态手势的分割、建模及识别;融合手势运动信息和皮肤颜色信息,进行复杂背景下的手势分割;通过结合手势的时序信息、运动表观以及形状表观,提出动态手势的时空表观模型,并提出基于颜色、运动以及形状等多模式信息的分层次融合策略抽取时空表观模型的参数,最后,提出动态时空规整算法用于手势识别,实验表明,利用上述提出的手势分割、建模、特征参数抽取及识别方法识别１２种手势,平均识别率高达９７％。     

Visual Recognition of Static/Dynamic Gesture: Gesture-Driven Editing System

This paper presents the visual recognition of static gesture (SG) or dynamic gesture (DG). Gesture is one of the most natural interface tools for human–computer interaction (HCI) as well as for communication between human beings. In order to implement a human-like interface, gestures could be recognized using only visual information such as the visual mechanism of human beings; SGs and DGs can be processed concurrently as well. This paper aims at recognizing hand gestures obtained from the visual images on a 2D image plane, without any external devices. Gestures are spotted by a task-specific state transition based on natural human articulation. SGs are recognized using image moments of hand posture, while DGs are recognized by analyzing their moving trajectories on the hidden Markov models (HMMs). We have applied our gesture recognition approach to gesture-driven editing systems operating in real time.     

Hand gesture recognition based on dynamic Bayesian network framework

In this paper, we propose a new method for recognizing hand gestures in a continuous video stream using a dynamic Bayesian network or DBN model. The proposed method of DBN-based inference is preceded by steps of skin extraction and modelling, and motion tracking. Then we develop a gesture model for one- or two-hand gestures. They are used to define a cyclic gesture network for modeling continuous gesture stream. We have also developed a DP-based real-time decoding algorithm for continuous gesture recognition. In our experiments with 10 isolated gestures, we obtained a recognition rate upwards of 99.59% with cross validation. In the case of recognizing continuous stream of gestures, it recorded 84% with the precision of 80.77% for the spotted gestures. The proposed DBN-based hand gesture model and the design of a gesture network model are believed to have a strong potential for successful applications to other related problems such as sign language recognition although it is a bit more complicated requiring analysis of hand shapes.     

An HMM-based threshold model approach for gesture recognition

A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14% reliability     

Static hand gesture recognition using neural networks

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.     

一种基于MEMS惯性传感器的手势识别方法

随着手机等移动电子设备的发展,应用于嵌入式平台的基于MEMS惯性传感器的手势识别成为一个研究热点.提出了一种简单有效的手势识别方法:通过分析手势的运动学特征,在线实时提取手势的加速度和角速度信号特征量,截取手势信号段,利用决策树分类器进行预分类,根据手势信号的变化规律实时识别具体的手势.该方法在20位实验者中获得了96％的平均准确率,手势识别时间小于0.01s.实验结果表明该算法在嵌入式平台下能快速准确地识别手势,满足了实时人机交互的要求.     

基于Leap Motion的动态手势识别方法

伴随虚拟现实（Virtual Reality,VR）技术的发展,以及人们对人机交互性能和体验感的要求提高,手势识别作为影响虚拟现实中交互操作的重要技术之一,其精确度急需提升^[1].针对当前手势识别方法在一些动作类似的手势识别中表现欠佳的问题,提出了一种多特征动态手势识别方法.该方法首先使用体感控制器Leap Motion追踪动态手势获取数据,然后在特征提取过程中增加对位移向量角度和拐点判定计数的提取,接着进行动态手势隐马尔科夫模型（Hidden Markov Model,HMM）的训练,最后根据待测手势与模型的匹配率进行识别.从实验结果中得出,该多特征识别方法能够提升相似手势的识别率.     

基于DTW的交警指挥手势识别方法

对于日益成熟的无人驾驶技术,如何快速准确地识别交警的手势成为无人驾驶领域中一个重要的研究内容。本文提出一种基于DTW算法的交警指挥手势识别方法。首先,使用Kinect传感器获取人体关节点数据并根据交警手势特点进行预处理,建立训练模板库;接着深入分析了该模板库的两个特征：类内高内聚性和类间低耦合性,由此给出交警指挥手势识别的实现步骤。实验结果表明,该方法能自动识别各种交警指挥手势,具有识别准确度较高、实时性较强、稳定性好的特点。     

基于语义分割与迁移学习的手势识别

针对复杂场景下深度相机环境要求高,可穿戴设备不自然,基于深度学习模型数据集样本少导致识别能力、鲁棒性欠佳的问题,提出了一种基于语义分割的深度学习模型进行手势分割结合迁移学习的神经网络识别的手势识别方法。通过对采集到的图像数据集首进行不同角度旋转,翻转等操作进行数据集样本增强,训练分割模型进行手势区域的分割,通过迁移学习卷积神经网络更好的提取手势特征向量,通过Softmax函数进行手势分类识别。通过4个人在不同背景下做的10个手势,实验结果表明: 针对复杂背景环境下能够正确的识别手势。