Gesture Recognition

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Hand Gesture Recognition Using Kinect

Author : Yi Li
ISBN : OCLC:841578512
Genre : Computer vision
File Size : 53.30 MB
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Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this thesis. a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HCR system is implemented with Microsoft Visual Studio 2010. Two different scenarios for HGR are provided: the Popular Gesture with nine gestures, and the Numbers with nine gestures. The system allows the users to select a scenario, and it is able to detect hand gestures made by users. to identify fingers, and to recognize the meanings of gestures, and to display the meanings and pictures on screen. The accuracy of the HGR system is from 84% to 99% with single hand gestures, and from 90% to 100% if both hands perform the same gesture at the same time. Because the depth sensor of Kinect is an infrared camera, the lighting conditions. signers' skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life.
Category: Computer vision

End To End Multiview Gesture Recognition For Autonomous Car Parking System

Author : Hassene Ben Amara
ISBN : OCLC:1122905518
Genre : Automated vehicles
File Size : 81.77 MB
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The use of hand gestures can be the most intuitive human-machine interaction medium. The early approaches for hand gesture recognition used device-based methods. These methods use mechanical or optical sensors attached to a glove or markers, which hinders the natural human-machine communication. On the other hand, vision-based methods are not restrictive and allow for a more spontaneous communication without the need of an intermediary between human and machine. Therefore, vision gesture recognition has been a popular area of research for the past thirty years. Hand gesture recognition finds its application in many areas, particularly the automotive industry where advanced automotive human-machine interface (HMI) designers are using gesture recognition to improve driver and vehicle safety. However, technology advances go beyond active/passive safety and into convenience and comfort. In this context, one of America's big three automakers has partnered with the Centre of Pattern Analysis and Machine Intelligence (CPAMI) at the University of Waterloo to investigate expanding their product segment through machine learning to provide an increased driver convenience and comfort with the particular application of hand gesture recognition for autonomous car parking. In this thesis, we leverage the state-of-the-art deep learning and optimization techniques to develop a vision-based multiview dynamic hand gesture recognizer for self-parking system. We propose a 3DCNN gesture model architecture that we train on a publicly available hand gesture database. We apply transfer learning methods to fine-tune the pre-trained gesture model on a custom-made data, which significantly improved the proposed system performance in real world environment. We adapt the architecture of the end-to-end solution to expand the state of the art video classifier from a single image as input (fed by monocular camera) to a multiview 360 feed, offered by a six cameras module. Finally, we optimize the proposed solution to work on a limited resources embedded platform (Nvidia Jetson TX2) that is used by automakers for vehicle-based features, without sacrificing the accuracy robustness and real time functionality of the system.
Category: Automated vehicles

Gesture Recognition

Author : Gilberto Coleman
ISBN : 1536134929
File Size : 58.30 MB
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In the opening chapter of Gesture Recognition: Performance, Applications and Features, the authors discuss gesture recognition and its role in the developing world of technology. The possibility of implementing a gesture detection application that works with people with special needs is examined, such as recognition of sign language for the hearing-impaired. Following this, the authors present their approach for face detection and tracking, user identification, facial feature extraction and head pose estimation as the low-level representation of facial gesture atomics. Additionally, an approach for a movement-based facial gestures recognition is presented, with results demonstrated through practical approaches. A later work explores spectral features from algebraic graph theory in static hand gesture recognition. Specifically, we apply a technique that uses the elements of the spectral matrix of the Laplacian to construct symmetric polynomials that are permutation invariants. The values of these polynomials can be used as graph features in a statistical learning pipeline that has the ability of distinguishing between distinct graphs and can reveal graph clusters. In the closing study, the authors developed two algorithms for the detection of pointing gestures and one approach for waving on this technological base and studied their functionality. The goal was to determine whether a combination of both strategies improves and stabilizes detection rates--

Face Detection And Gesture Recognition For Human Computer Interaction

Author : Ming-Hsuan Yang
ISBN : 9781461514237
Genre : Computers
File Size : 27.21 MB
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Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.
Category: Computers

Gesture Recognition

Author : Sergio Escalera
ISBN : 9783319570211
Genre : Computers
File Size : 64.33 MB
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This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.
Category: Computers

Gesture Recognition

Author : Amit Konar
ISBN : 9783319622125
Genre : Computers
File Size : 73.76 MB
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This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.
Category: Computers

Robust Hand Gesture Recognition For Robotic Hand Control

Author : Ankit Chaudhary
ISBN : 9789811047985
Genre : Technology & Engineering
File Size : 87.49 MB
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This book focuses on light invariant bare hand gesture recognition while there is no restriction on the types of gestures. Observations and results have confirmed that this research work can be used to remotely control a robotic hand using hand gestures. The system developed here is also able to recognize hand gestures in different lighting conditions. The pre-processing is performed by developing an image-cropping algorithm that ensures only the area of interest is included in the segmented image. The segmented image is compared with a predefined gesture set which must be installed in the recognition system. These images are stored and feature vectors are extracted from them. These feature vectors are subsequently presented using an orientation histogram, which provides a view of the edges in the form of frequency. Thereby, if the same gesture is shown twice in different lighting intensities, both repetitions will map to the same gesture in the stored data. The mapping of the segmented image's orientation histogram is firstly done using the Euclidian distance method. Secondly, the supervised neural network is trained for the same, producing better recognition results. An approach to controlling electro-mechanical robotic hands using dynamic hand gestures is also presented using a robot simulator. Such robotic hands have applications in commercial, military or emergency operations where human life cannot be risked. For such applications, an artificial robotic hand is required to perform real-time operations. This robotic hand should be able to move its fingers in the same manner as a human hand. For this purpose, hand geometry parameters are obtained using a webcam and also using KINECT. The parameter detection is direction invariant in both methods. Once the hand parameters are obtained, the fingers’ angle information is obtained by performing a geometrical analysis. An artificial neural network is also implemented to calculate the angles. These two methods can be used with only one hand, either right or left. A separate method that is applicable to both hands simultaneously is also developed and fingers angles are calculated. The contents of this book will be useful for researchers and professional engineers working on robotic arm/hand systems.
Category: Technology & Engineering

Motion Tracking And Gesture Recognition

Author : Carlos Travieso-Gonzalez
ISBN : 9789535133773
Genre : Computers
File Size : 40.30 MB
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Nowadays, the technological advances allow developing many applications on different fields. In this book Motion Tracking and Gesture Recognition, two important fields are shown. Motion tracking is observed by a hand-tracking system for surgical training, an approach based on detection of dangerous situation by the prediction of moving objects, an approach based on human motion detection results and preliminary environmental information to build a long-term context model to describe and predict human activities, and a review about multispeaker tracking on different modalities. On the other hand, gesture recognition is shown by a gait recognition approach using Kinect sensor, a study of different methodologies for studying gesture recognition on depth images, and a review about human action recognition and the details about a particular technique based on a sensor of visible range and with depth information.
Category: Computers

Statistical Hand Gesture Recognition System Using The Leap Motion Controller

Author : Michael Dimartino
ISBN : OCLC:1191854518
Genre :
File Size : 37.54 MB
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As technology continues to improve, hand gesture recognition as a form of humancomputer interaction is becoming more and more feasible. One such piece of technology, the Leap Motion Controller, provides 3D tracking data of the hands through an easy-to-use API. This thesis presents an application that uses Leap Motion tracking data to learn and recognize static and dynamic hand gestures. Gestures are recognized using statistical pattern recognition. Each gesture is defined by a set of features including fingertip positions, hand orientation, and movement. Given sufficient training data, the similarity between two gestures is measured by comparing each of their corresponding features. Two separate implementations are presented for dealing with the temporal aspect of dynamic gestures. Users are able to interact with the system using a convenient graphical user interface. The accuracy of the system was experimentally tested with the help of two separate test participants: one for the training phase and one for the recognition phase. All test gestures (both static and dynamic) were successfully recognized with minimal training data. In some cases, additional gestures were mistakenly recognized.

Gesture Based Human Computer Interaction And Simulation

Author : Miguel Sales Dias
ISBN : 9783540928645
Genre : Computers
File Size : 35.22 MB
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This book constitutes the thoroughly refereed post-proceedings of the 7th International Workshop on Gesture-Based Human-Computer Interaction and Simulation, GW 2007, held in Lisbon, Portugal, in May 2007. The 31 revised papers presented were carefully selected from 53 submissions. The papers are organized in topical sections on analysis and synthesis of gesture; theoretical aspects of gestural communication and interaction; vision-based gesture recognition; sign language processing; gesturing with tangible interfaces and in virtual and augmented reality; gesture for music and performing arts; gesture for therapy and rehabilitation; and gesture in Mobile computing and usability studies.
Category: Computers

Structural Syntactic And Statistical Pattern Recognition

Author : Niels da Vitoria Lobo
ISBN : 9783540896883
Genre : Computers
File Size : 54.88 MB
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This book constitutes the refereed proceedings of the 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 and the 7th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008. The 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.
Category: Computers

Gesture Based Communication In Human Computer Interaction

Author : Antonio Camurri
ISBN : 9783540210726
Genre : Computers
File Size : 71.59 MB
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Research on the multifaceted aspects of modeling, analysis, and synthesis of - man gesture is receiving growing interest from both the academic and industrial communities. On one hand, recent scienti?c developments on cognition, on - fect/emotion, on multimodal interfaces, and on multimedia have opened new perspectives on the integration of more sophisticated models of gesture in c- putersystems.Ontheotherhand,theconsolidationofnewtechnologiesenabling “disappearing” computers and (multimodal) interfaces to be integrated into the natural environments of users are making it realistic to consider tackling the complex meaning and subtleties of human gesture in multimedia systems, - abling a deeper, user-centered, enhanced physical participation and experience in the human-machine interaction process. The research programs supported by the European Commission and s- eral national institutions and governments individuated in recent years strategic ?elds strictly concerned with gesture research. For example, the DG Infor- tion Society of the European Commission ( supports several initiatives, such as the “Disappearing Computer” and “Presence” EU-IST FET (Future and Emerging Technologies), the IST program “Interfaces & Enhanced Audio-Visual Services” (see for example the project MEGA, Multisensory - pressive Gesture Applications,, and the IST strategic - jective “Multimodal Interfaces.” Several EC projects and other funded research are represented in the chapters of this book. Awiderangeofapplicationscanbene?tfromadvancesinresearchongesture, from consolidated areas such as surveillance to new or emerging ?elds such as therapy and rehabilitation, home consumer goods, entertainment, and aud- visual, cultural and artistic applications, just to mention only a few of them.
Category: Computers

Gesture Recognition A Review Focusing On Sign Language In A Mobile Context

Author :
ISBN : OCLC:1051919277
Genre :
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Highlights: Most used techniques: skin detection, brute force comparison and SVM. Classification scores: 39%-99% (static gestures); 61.3%-97.4% (dynamic gestures with special hardware). Predominant environment consisted of simple background and controlled light. Static and dynamic gestures are not recognized by the same approach. Gestures recognized and datasets are too small for real-world scenarios. Abstract: Sign languages, which consist of a combination of hand movements and facial expressions, are used by deaf persons around the world to communicate. However, hearing persons rarely know sign languages, creating barriers to inclusion. The increasing progress of mobile technology, along with new forms of user interaction, opens up possibilities for overcoming such barriers, particularly through the use of gesture recognition through smartphones. This Literature Review discusses works from 2009 to 2017 that present solutions for gesture recognition in a mobile context as well as facial recognition in sign languages. Among a diversity of hardware and techniques, sensor-based gloves were the most used special hardware, along with brute force comparison to classify gestures. Works that did not adopt special hardware mostly used skin color for feature extraction in gesture recognition. Classification algorithms included: Support Vector Machines, Hierarchical Temporal Memory and Feedforward backpropagation neural network, among others. Recognition of static gestures typically achieved results higher than 80%. Fewer papers recognized dynamic gestures, obtaining results above 90%. However, most experiments were performed under controlled environments, with specific lighting conditions, and were only using a small set of gestures. In addition, the majority of works dealt with a simple background and used special hardware (which is often cumbersome for the user) to facilitate feature extraction. Facial expression recognition achieved high classification results using Random-Forest and Multi-layer Perceptron. Despite the progress being made with the increasing interest in gesture recognition, there are still important gaps to be addressed in the context of sign languages. Besides improving usability and efficacy of the solutions, recognition of facial expression and of both static and dynamic gestures in complex backgrounds must be considered.

Towards Chereme Based Dynamic Sign Language Gesture Recognition System

Author : Addmore Machanja
ISBN : 3844318712
Genre :
File Size : 75.85 MB
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"Hand gestures are a natural and intuitive way of human communication. Motivated by the achievements made towards automatic speech recognition, and by the ease with which people sign, many researchers started working on sign language recognition systems. Besides, technologies used to gesture recognition system pose as an alternative to the cumbersome and the failure prone mechanical devices that are currently used as human-machine interface devices. Most of the available gesture recognition systems represent each sign language gesture with an individual gesture model. Such systems can only recognize a limited number of dynamic sign language gestures. It is cumbersome to build and maintain a gesture recognition system that uses thousands and thousands of individual gesture models. Sign language linguists argue that all sign language gestures are derived from small sets of reusable components, the cheremes. However, computer vision is such as ill-posed problem to the extent that it very difficult to sufficiently detect the basic gesture components from image data during image processing. In most cases important gesture information is lost as a result of occlusion, image noise or during the process of transforming 3D world views into 2D projections. Gesture recognition systems that recognize a large vocabulary of sign language gestures can only be built if we devise image processing algorithms that achieve high quality hand segmentation and tracking. This research presents a multi-cue based segmentation method that helps to improve the extraction of the hand-shape chereme. A Support Vector Machine (SVM) is then used for verifying the hand-shapes that are associated with each input gesture. Hand sementation results directly affect the extraction of the hand position and hand movement cheremes. The hand movement patterns are learnt and recognized through the Hidden Markov Model (HMM). A sequence of cheremes that represent each geture is used to build an online gesture dictionary that helps the gesture recognition module to classify the input gestures. In this research, video footages of signing people are used as input gestures. Since the meaning of a gesture differs from society to society in this project we only focuses on dynamic gestures from the South African Sign Language (SASL). The technologies used in this project will find many applications in various fields of Human Computer Interaction (HCI)". Summary on page iv.

Using Photoplethysmography For Simple Hand Gesture Recognition

Author : Karthik Subramanian
ISBN : OCLC:1190720586
Genre : Hand
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"A new wearable band is developed which uses three Photoplethysmography (PPG) sensors for the purpose of hand gesture recognition (HGR). These sensors are typically used for heart rate estimation and detection of cardiovascular diseases. Heart rate estimates obtained from these sensors are disregarded when the arm is in motion on account of artifacts. This research suggests and demonstrates that these artifacts are repeatable in nature based on the gestures performed. A comparative study is made between the developed band and the Myo Armband which uses surface-Electromyography (s-EMG) for gesture recognition. Based on the results of this paper which employs supervised machine learning techniques, it can be seen that PPG can be utilized as a viable alternative modality for gesture recognition applications."--Abstract.
Category: Hand

An Overview Of Hand Gestures Recognition System Techniques

Author :
ISBN : OCLC:1051994974
Genre :
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Abstract: Hand gesture recognition system has evolved tremendously in the recent few years because of its ability to interact with machine efficiently. Mankind tries to incorporate human gestures into modern technology by searching and finding a replacement of multi touch technology which does not require any touching movement on screen. This paper presents an overview on several methods to realize hand gesture recognition by using three main modules: camera and segmentation module, detection module and feature extraction module. There are many methods which can be used to get the respective results depending on its advantages. Summary of previous research and results of hand gesture methods as well as comparison between gesture recognition are also given in this paper.