MURAL - Maynooth University Research Archive Library



    Continuous recognition of motion based gestures in sign language


    Kelly, Daniel and McDonald, John and Markham, Charles (2009) Continuous recognition of motion based gestures in sign language. In: IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009. IEEE, pp. 1073-1080. ISBN 9781424444427

    [img]
    Preview
    Download (725kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    We present a novel and robust system for recognizing two handed motion based gestures performed within continuous sequences of sign language. While recognition of valid sign sequences is an important task in the overall goal of machine recognition of sign language, detection of movement epenthesis is important in the task of continuous recognition of natural sign language. We propose a framework for recognizing valid sign segments and identifying movement epenthesis. Our system utilizes a single HMM threshold model, per hand, to detect movement epenthesis. Further to this, we develop a novel technique to utilize the threshold model and dedicated gesture HMMs to recognize gestures within continuous sign language sentences. Experiments show that our system has a gesture detection ratio of 0.956 and a reliability measure of 0.932 when spotting 8 different signs from 240 video clips.

    Item Type: Book Section
    Keywords: image recognition; feature extraction; hidden Markov models; image motion analysis;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8340
    Identification Number: https://doi.org/10.1109/ICCVW.2009.5457585
    Depositing User: John McDonald
    Date Deposited: 15 Jun 2017 15:57
    Publisher: IEEE
    Refereed: Yes
    Funders: Irish Research Council for Science Engineering and Technology (IRCSET)
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year