The design of action recognition algorithms often relies on knowledge of the particular problem, which is not always available. Moreover, algorithms usually incorporate a number of parameters, which influence their performance. To solve these problems, the possibility of developing more general action recognition algorithms by systematic reduction of complexity of human motion, instead of designing more and more complex algorithms is explored. The key for reducing complexity in systematic decomposition of human motion to different scales, each representing different level of motion detail is seen. A human action is assumed to influence different scales of motion, and they should be observed that way. The features, obtained on different scales of motion can be joined together to represent the complex motion in uniform and manageable way. Our approach was tested in the sports domain, on a particular problem of detecting the action of athlete hitting the ball with a racquet in the game of squash. Video recordings of actual tournament match were used, and manual annotations were provided by squash expert as a ground truth.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Scale-based human motion representation for action recognition


    Beteiligte:
    Pers, J. (Autor:in) / Vuckovic, G. (Autor:in) / Dezman, B. (Autor:in) / Kovacic, S. (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    414928 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Scale-Based Human Motion Representation for Action Recognition

    Pers, J. / Vuckovic, G. / Dezman, B. et al. | British Library Conference Proceedings | 2003


    Human action recognition based on aggregated local motion estimates

    Lucena, M. | British Library Online Contents | 2012


    Deep temporal motion descriptor (DTMD) for human action recognition

    Nida, Nudrat / Yousaf, Muhammad Haroon / Irtaza, Aun et al. | BASE | 2020

    Freier Zugriff

    MoFAP: A Multi-level Representation for Action Recognition

    Wang, L. / Qiao, Y. / Tang, X. | British Library Online Contents | 2016


    Human Action Recognition Based on CRM

    Huang, J. / Xia, L. / Zhang, W. | British Library Online Contents | 2013