This project aims to explore action recognition through a deep learning model generated by Convolutional Neural Networks, establishing the foundation for human-robot interaction in a scenario where Unmanned Aerial Vehicles (UAV) are controlled exclusively by visual commands. The model analyzes images captured by an onboard camera using and classifies them into nine categories. Each category issues a specific command based on human actions performed by individuals properly equipped with personal protective equipment. The results demonstrate the feasibility of the proposed approach, opening room for improvements aiming its use in more complex scenarios.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Recognizing Human Actions: A Deep Learning Model for UAV Piloting




    Erscheinungsdatum :

    13.11.2024


    Format / Umfang :

    3576278 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Recognizing human actions in a static room

    Ayers, D. / Shah, M. | IEEE | 1998


    Recognizing Human Actions in a Static Room

    Ayers, D. / Shah, M. | British Library Conference Proceedings | 1998


    DRONE PILOTING MACHINE, AND PILOTING PROGRAM

    MIYAGI RYO / WAKE CHIHIRO / YAGISHITA HIROSHI | Europäisches Patentamt | 2021

    Freier Zugriff

    DRONE PILOTING MACHINE, AND PILOTING PROGRAM

    MIYAGI RYO / WAKE CHIHIRO / YANAGISHITA HIROSHI | Europäisches Patentamt | 2020

    Freier Zugriff

    AR PILOTING SYSTEM AND AR PILOTING METHOD

    ADACHI SATOSHI / SEKINE EISUKE / SUZUKI KATSUHIRO et al. | Europäisches Patentamt | 2022

    Freier Zugriff