HMMs are often used for gesture recognition because of the robustness. However, the computational cost and accuracy of recognition are important for real applications such as gesture recognition, speech recognition or virtual reality. In this paper, we propose methods for performance improvement of gesture recognition using HMMs. For the computational cost, we use KL transform to compress the input information and propose a recursive calculation method for the HMMs' probabilities. For the accuracy of recognition, we use an automaton layered up on HMMs to deal with context information of gestures. We also show experimental results to make the efficiency of our methods clear.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-time context-based gesture recognition using HMM and automaton


    Contributors:
    Iwai, Y. (author) / Shimizu, H. (author) / Yachida, M. (author)


    Publication date :

    1999-01-01


    Size :

    967356 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    3-D Real-Time Gesture Recognition Using Proximity Spaces

    Huber, E. / IEEE | British Library Conference Proceedings | 1996



    Real-time gesture recognition system and application

    Wah Ng, C. / Ranganath, S. | British Library Online Contents | 2002


    Real-Time Dynamic Gesture Recognition based on Boundary-Constraint Dynamic Time Warping

    Cheng, Chunling / Liu, Yangjunwu / Yang, Jian et al. | IEEE | 2019


    A Real-Time Applicable Dynamic Hand Gesture Recognition Framework

    Kopinski, Thomas / Gepperth, Alexander / Handmann, Uwe | IEEE | 2015