This paper research on the motion recognition based on HMM with Kinect. Kinect provides skeletal data consist of 3D body joints with its lower price and convenience. In this work, several methods are used to determine the optimal subset of features among Cartesian coordinates, distance to hip center, velocity, angle and angular velocity, in order to improve the recognition rate. K-means is used for vector quantization and HMM is used as recognition method. HMM is an effective signal processing method which contains time calibration, provides a learning mechanism and recognition ability. Cluster numbers of K-means, structure and state numbers of HMM are optimized as well. The proposed methods are applied to the MSR Action3D dataset. Results show that the proposed methods obtain better recognition accuracy than the state of the art methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Human motion recognition based on features and models selected HMM


    Beteiligte:
    Lu, Haixiang (Autor:in) / Zhou, Hongjun (Autor:in)

    Kongress:

    Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part I ; 2014 ; China,China


    Erschienen in:

    Erscheinungsdatum :

    04.03.2015





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Hybrid models for human motion recognition

    Fanti, C. / Zelnik-Manor, L. / Perona, P. | IEEE | 2005


    Conditional models for contextual human motion recognition

    Sminchisescu, C. / Kanaujia, A. / Zhiguo Li, et al. | IEEE | 2005


    Conditional models for contextual human motion recognition

    Sminchisescu, C. / Kanaujia, A. / Metaxas, D. | British Library Online Contents | 2006


    Hybrid Dynamical Models of Human Motion for the Recognition of Human Gaits

    Bissacco, A. / Soatto, S. | British Library Online Contents | 2009