Robust Real Time Hand Tracking And Gesture Recognition Using Smart Snakes
Download Robust Real Time Hand Tracking And Gesture Recognition Using Smart Snakes full books in PDF, epub, and Kindle. Read online free Robust Real Time Hand Tracking And Gesture Recognition Using Smart Snakes ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Real-time 2D Static Hand Gesture Recognition and 2D Hand Tracking for Human-Computer Interaction
| Author | : Pavel Alexandrovich Popov |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
The topic of this thesis is Hand Gesture Recognition and Hand Tracking for user interface applications. 3 systems were produced, as well as datasets for recognition and tracking, along with UI applications to prove the concept of the technology. These represent significant contributions to resolving the hand recognition and tracking problems for 2d systems. The systems were designed to work in video only contexts, be computationally light, provide recognition and tracking of the user's hand, and operate without user driven fine tuning and calibration. Existing systems require user calibration, use depth sensors and do not work in video only contexts, or are computationally heavy requiring GPU to run in live situations. A 2-step static hand gesture recognition system was created which can recognize 3 different gestures in real-time. A detection step detects hand gestures using machine learning models. A validation step rejects false positives. The gesture recognition system was combined with hand tracking. It recognizes and then tracks a user's hand in video in an unconstrained setting. The tracking uses 2 collaborative strategies. A contour tracking strategy guides a minimization based template tracking strategy and makes it real-time, robust, and recoverable, while the template tracking provides stable input for UI applications. Lastly, an improved static gesture recognition system addresses the drawbacks due to stratified colour sampling of the detection boxes in the detection step. It uses the entire presented colour range and clusters it into constituent colour modes which are then used for segmentation, which improves the overall gesture recognition rates. One dataset was produced for static hand gesture recognition which allowed for the comparison of multiple different machine learning strategies, including deep learning. Another dataset was produced for hand tracking which provides a challenging series of user scenarios to test the gesture recognition and hand tracking system. Both datasets are significantly larger than other available datasets. The hand tracking algorithm was used to create a mouse cursor control application, a paint application for Android mobile devices, and a FPS video game controller. The latter in particular demonstrates how the collaborating hand tracking can fulfill the demanding nature of responsive aiming and movement controls.
Novel Methods for Robust Real-time Hand Gesture Interfaces
| Author | : Nathaniel Sean Rossol |
| Publisher | : |
| Total Pages | : 110 |
| Release | : 2015 |
| Genre | : Computer vision |
| ISBN | : |
Real-time control of visual display systems via mid-air hand gestures offers many advantages over traditional interaction modalities. In medicine, for example, it allows a practitioner to adjust display values, e.g. contrast or zoom, on a medical visualization interface without the need to re-sterilize the interface. However, there are many practical challenges that make such interfaces non-robust including poor tracking due to frequent occlusion of fingers, interference from hand-held objects, and complex interfaces that are difficult for users to learn to use efficiently. In this work, various techniques are explored for improving the robustness of computer interfaces that use hand gestures. This work is focused predominately on real-time markerless Computer Vision (CV) based tracking methods with an emphasis on systems with high sampling rates. First, we explore a novel approach to increase hand pose estimation accuracy from multiple sensors at high sampling rates in real-time. This approach is achieved through an intelligent analysis of pose estimations from multiple sensors in a way that is highly scalable because raw image data is not transmitted between devices. Experimental results demonstrate that our proposed technique significantly improves the pose estimation accuracy while still maintaining the ability to capture individual hand poses at over 120 frames per second. Next, we explore techniques for improving pose estimation for the purposes of gesture recognition in situations where only a single sensor is used at high sampling rates without image data. In this situation, we demonstrate an approach where a combination of kinematic constraints and computed heuristics are used to estimate occluded keypoints to produce a partial pose estimation of a user's hand which is then used with our gestures recognition system to control a display. The results of our user study demonstrate that the proposed algorithm significantly improves the gesture recognition rate of the setup. We then explore gesture interface designs for situations where the user may (or may not) have a large portion of their hand occluded by a hand-held tool while gesturing. We address this challenge by developing a novel interface that uses a single set of gestures designed to be equally effective for fingers and hand-held tools without the need for any markers. The effectiveness of our approach is validated through a user study on a group of people given the task of adjusting parameters on a medical image display. Finally, we examine improving the efficiency of training for our interfaces by automatically assessing key user performance metrics (such as dexterity and confidence), and adapting the interface accordingly to reduce user frustration. We achieve this through a framework that uses Bayesian networks to estimate values for abstract hidden variables in our user model, based on analysis of data recorded from the user during operation of our system.
Real-time Dynamic Hand Shape Gesture Controller
| Author | : Rajesh Radhakrishnan |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
The main objective of this thesis is to build a real time gesture recognition system which can spot and recognize specific gestures from continuous stream of input video. We address the recognition of single handed dynamic gestures. We have considered gestures which are sequences of distinct hand poses. Gestures are classified based on their hand poses and its nature of motion. The recognition strategy uses a combination of spatial hand shape recognition using chamfer distance measure and temporal characteristics through dynamic programming. The system is fairly robust to background clutter and uses skin color for tracking. Gestures are an important modality for human-machine communication, and robust gesture recognition can be an important component of intelligent homes and assistive environments in general. Challenging task in a robust recognition system is the amount of unique gesture classes that the system can recognize accurately. Our problem domain is two dimensional tracking and recognition with a single static camera. We also address the reliability of the system as we scale the size of gesture vocabulary. Our system is based on supervised learning, both detection and recognition uses the existing trained models. The hand tracking framework is based on non-parametric histogram bin based approach. A coarser histogram bin containing skin and non-skin models of size 32x32x32 was built. The histogram bins were generated by using samples of skin and non-skin images. The tracker framework effectively finds the moving skin locations as it integrates both the motion and skin detection. Hand shapes are another important modality of our gesture recognition system. Hand shapes can hold important information about the meaning of a gesture, or about the intent of an action. Recognizing hand shapes can be a very challenging task, because the same hand shape may look very different in different images, depending on the view point of the camera. We use chamfer matching of edge extracted hand regions to compute the minimum chamfer matching score. Dynamic Programming technique is used align the temporal sequences of gesture. In this paper, we propose a novel hand gesture recognition system where in user can specify his/her desired gestures vocabulary. The contributions made to the gesture recognition framework are, user-chosen gesture vocabulary (i.e) user is given an option to specify his/her desired gesture vocabulary, confusability analysis of gesture (i.e) During training, if user provides similar gesture pattern for two different gesture patterns the system automatically alerts the user to provide a different gesture pattern for a specific class, novel methodology to combine both hand shape and motion trajectory for recognition, hand tracker (using motion and skin color detection) aided hand shape recognition. The system runs in real time with frame rate of 15 frames per second in debug mode and 17 frames per second in release mode. The system was built in a normal hardware configuration with Microsoft Visual Studio, using OpenCV and C++. Experimental results establish the effectiveness of the system.
Managing Interactions in Smart Environments
| Author | : Paddy Nixon |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 257 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447107438 |
Research into Smart Buildings and Spaces has increased rapidly over the last few years. This volume aims to address the convergence of research in Distributed Systems, Robotics and Human Centred computing within the domain of smart buildings and present a unique opportunity to investigate work that crosses the boundaries of these disciplines. It provides an overview of progress in a fast-moving area, by bringing together researchers, implementors and practitioners and the papers draw together the developments and concerns of those working on the different aspects of smart environments, as well as providing views on the future prospects for work in this area.
Robust Real-time Hands-and-face Detection for Human Robot Interaction
| Author | : SeyedMehdi MohaimenianPour |
| Publisher | : |
| Total Pages | : 91 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
With recent advances, robots have become more affordable and intelligent, which expands their application domain and number of consumers. Having robots around us in our daily lives creates a demand for an interaction system for communicating humans' intentions and commands to robots. We are interested in interactions that are easy, intuitive, and do not require the human to use any additional equipment. We present a robust real-time system for visual detection of hands and faces in RGB and gray-scale images based on a Deep Convolutional Neural Network. This system is designed to meet the requirements of a hands-free interface to UAVs described below that could be used for communicating to other robots equipped with a monocular camera using only hands and face gestures without any extra instruments. This work is accompanied by a novel hands-and-faces detection dataset gathered and labelled from a wide variety of sources including our own Human-UAV interaction videos, and several third-party datasets. By training our model on all these data, we obtain qualitatively good detection results in terms of both accuracy and speed on a commodity GPU. The same detector gives state-of-the-art accuracy and speed in a hand-detection benchmark and competitive results in a face detection benchmark. To demonstrate its effectiveness for Human-Robot Interaction we describe its use as the input to a novel, simple but practical gestural Human-UAV interface for static gesture detection based on hand position relative to the face. A small vocabulary of hand gestures is used to demonstrate our end-to-end pipeline for un-instrumented human-UAV interaction useful for entertainment or industrial applications. All software, training and test data produced for this thesis is released as an Open Source contribution.
Dual-sensor Approaches for Real-time Robust Hand Gesture Recognition
| Author | : Kui Liu |
| Publisher | : |
| Total Pages | : 198 |
| Release | : 2015 |
| Genre | : Gesture |
| ISBN | : |
The use of hand gesture recognition has been steadily growing in various human-computer interaction applications. Under realistic operating conditions, it has been shown that hand gesture recognition systems exhibit recognition rate limitations when using a single sensor. Two dual-sensor approaches have thus been developed in this dissertation in order to improve the performance of hand gesture recognition under realistic operating conditions. The first approach involves the use of image pairs from a stereo camera setup by merging the image information from the left and right camera, while the second approach involves the use of a Kinect depth camera and an inertial sensor by fusing differing modality data within the framework of a hidden Markov model. The emphasis of this dissertation has been on system building and practical deployment. More specifically, the major contributions of the dissertation are: (a) improvement of hand gestures recognition rates when using a pair of images from a stereo camera compared to when using a single image by fusing the information from the left and right images in a complementary manner, and (b) improvement of hand gestures recognition rates when using a dual-modality sensor setup consisting of a Kinect depth camera and an inertial body sensor compared to the situations when each sensor is used individually on its own. Experimental results obtained indicate that the developed approaches generate higher recognition rates in different backgrounds and lighting conditions compared to the situations when an individual sensor is used. Both approaches are designed such that the entire recognition system runs in real-time on PC platform.
Computer Vision for Human-Machine Interaction
| Author | : Roberto Cipolla |
| Publisher | : Cambridge University Press |
| Total Pages | : 364 |
| Release | : 1998-07-13 |
| Genre | : Computers |
| ISBN | : 9780521622530 |
Leading scientists describe how advances in computer vision can change how we interact with computers.
Challenges and Applications for Hand Gesture Recognition
| Author | : Kane, Lalit |
| Publisher | : IGI Global |
| Total Pages | : 249 |
| Release | : 2022-03-25 |
| Genre | : Computers |
| ISBN | : 1799894363 |
Due to the rise of new applications in electronic appliances and pervasive devices, automated hand gesture recognition (HGR) has become an area of increasing interest. HGR developments have come a long way from the traditional sign language recognition (SLR) systems to depth and wearable sensor-based electronic devices. Where the former are more laboratory-oriented frameworks, the latter are comparatively realistic and practical systems. Based on various gestural traits, such as hand postures, gesture recognition takes different forms. Consequently, different interpretations can be associated with gestures in various application contexts. A considerable amount of research is still needed to introduce more practical gesture recognition systems and associated algorithms. Challenges and Applications for Hand Gesture Recognition highlights the state-of-the-art practices of HGR research and discusses key areas such as challenges, opportunities, and future directions. Covering a range of topics such as wearable sensors and hand kinematics, this critical reference source is ideal for researchers, academicians, scholars, industry professionals, engineers, instructors, and students.
Progress in Gestural Interaction
| Author | : Philip A. Harling |
| Publisher | : Springer |
| Total Pages | : 264 |
| Release | : 1997 |
| Genre | : Computers |
| ISBN | : |
Progress in Gestural Interaction contains papers presented at the first Gesture Workshop, which was designed to bring together researchers working on gesture-based interfaces and gestural interaction and to provide a forum for the presentation and exchange of ideas and research currently in progress. It encompassed all aspects of gestural interaction, including:- what are gestures?; appropriateness of gestures used in interfaces; interactional issues; suitable applications; sign-language recognition. Papers are presented from the UK, France, Germany, Italy, USA and Ireland to provide an international viewpoint and the book will be of interest to researchers in the fields of gestural interaction, human-computer interaction, multi-modal interfaces, automatic sign language interpretation and pattern recognition. It could also be a useful supplementary text for courses on multi-modal human-computer interaction.
