211 research outputs found
Real-Time Head Gesture Recognition on Head-Mounted Displays using Cascaded Hidden Markov Models
Head gesture is a natural means of face-to-face communication between people
but the recognition of head gestures in the context of virtual reality and use
of head gesture as an interface for interacting with virtual avatars and
virtual environments have been rarely investigated. In the current study, we
present an approach for real-time head gesture recognition on head-mounted
displays using Cascaded Hidden Markov Models. We conducted two experiments to
evaluate our proposed approach. In experiment 1, we trained the Cascaded Hidden
Markov Models and assessed the offline classification performance using
collected head motion data. In experiment 2, we characterized the real-time
performance of the approach by estimating the latency to recognize a head
gesture with recorded real-time classification data. Our results show that the
proposed approach is effective in recognizing head gestures. The method can be
integrated into a virtual reality system as a head gesture interface for
interacting with virtual worlds
Touché: Data-Driven Interactive Sword Fighting in Virtual Reality
VR games offer new freedom for players to interact naturally using motion. This makes it harder to design games that react to player motions convincingly. We present a framework for VR sword fighting experiences against a virtual character that simplifies the necessary technical work to achieve a convincing simulation. The framework facilitates VR design by abstracting from difficult details on the lower “physical” level of interaction, using data-driven models to automate both the identification of user actions and the synthesis of character animations. Designers are able to specify the character's behaviour on a higher “semantic” level using parameterised building blocks, which allow for control over the experience while minimising manual development work. We conducted a technical evaluation, a questionnaire study and an interactive user study. Our results suggest that the framework produces more realistic and engaging interactions than simple hand-crafted interaction logic, while supporting a controllable and understandable behaviour design
Hidden-Markov-Models-Based Dynamic Hand Gesture Recognition
This paper is concerned with the recognition of dynamic hand gestures. A method based on Hidden Markov Models (HMMs) is presented for dynamic gesture trajectory modeling and recognition. Adaboost algorithm is used to detect the user's hand and a contour-based hand tracker is formed combining condensation and partitioned sampling. Cubic B-spline is adopted to approximately fit the trajectory points into a curve. Invariant curve moments as global features and orientation as local features are computed to represent the trajectory of hand gesture. The proposed method can achieve automatic hand gesture online recognition and can successfully reject atypical gestures. The experimental results show that the proposed algorithm can reach better recognition results than the traditional hand recognition method
HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
We propose a two-stage convolutional neural network (CNN) architecture for
robust recognition of hand gestures, called HGR-Net, where the first stage
performs accurate semantic segmentation to determine hand regions, and the
second stage identifies the gesture. The segmentation stage architecture is
based on the combination of fully convolutional residual network and atrous
spatial pyramid pooling. Although the segmentation sub-network is trained
without depth information, it is particularly robust against challenges such as
illumination variations and complex backgrounds. The recognition stage deploys
a two-stream CNN, which fuses the information from the red-green-blue and
segmented images by combining their deep representations in a fully connected
layer before classification. Extensive experiments on public datasets show that
our architecture achieves almost as good as state-of-the-art performance in
segmentation and recognition of static hand gestures, at a fraction of training
time, run time, and model size. Our method can operate at an average of 23 ms
per frame
Designing racing game controller by image-based hand gesture recognition
Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: Meysam Madadi[en] This thesis is focused on exploring how hand gesture recognition can be used to replace controllers in racing games. The goal is to understand how to develop a system that is accurate, allowing for more responsive control when driving virtual vehicles. The first step of the research project is to analyze existing gesture recognition technologies, such as the Microsoft Kinect, and how they can be used in racing games.
By studying existing implementations, the thesis aims to gain key concepts that can help improve the user's experience. The research will also investigate various hardware requirements, such as camera placements and sensors, that would be necessary for the system to function effectively in a racing game environment. Once the research requirements are established, testing will be carried out to evaluate how effective gesture-based control
systems are compared to traditional controllers. The results of these tests will be analyzed to evaluate how well the system performs compared to existing controller-based racing games.
The ultimate goal of this thesis is to create a more natural form of control that allows players to focus on the thrill of racing without worrying about button presses or joystick movements
Grasps recognition and evaluation of stroke patients for supporting rehabilitation therapy
Copyright © 2014 Beatriz Leon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Stroke survivors often suffer impairments on their wrist and hand. Robot-mediated rehabilitation techniques have been proposed as a way to enhance conventional therapy, based on intensive repeated movements. Amongst the set of activities of daily living, grasping is one of the most recurrent. Our aim is to incorporate the detection of grasps in the machine-mediated rehabilitation framework so that they can be incorporated into interactive therapeutic games. In this study, we developed and tested a method based on support vector machines for recognizing various grasp postures wearing a passive exoskeleton for hand and wrist rehabilitation after stroke. The experiment was conducted with ten healthy subjects and eight stroke patients performing the grasping gestures. The method was tested in terms of accuracy and robustness with respect to intersubjects' variability and differences between different grasps. Our results show reliable recognition while also indicating that the recognition accuracy can be used to assess the patients' ability to consistently repeat the gestures. Additionally, a grasp quality measure was proposed to measure the capabilities of the stroke patients to perform grasp postures in a similar way than healthy people. These two measures can be potentially used as complementary measures to other upper limb motion tests.Peer reviewedFinal Published versio
End-to-End Multiview Gesture Recognition for Autonomous Car Parking System
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
In the Presence of Images
I capture an image. I forget the image. I attempt to remember. Nothing. The images sole purpose was to help me remember, yet it cant. Has it failed, or have I? I begin to speculate how the forgotten image functions in the present, liberated from its associated past.
Through the use of digitally archived images that have been forgotten since their capture, In the Presence of Images is a travelling exhibition that explores the digital revolution and its potential effects on memory. Through the processing of photographs and videos sourced from my personal digital archive, images are created to form a space for discussion, contemplation, and speculation, encouraging the viewer to question the capacities of forgotten imagery and to become cognizant of the technologies we entrust with more and more data everyday
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