In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations called SCORP was collected. Deep models are evaluated with various radar input representations. Our proposed approach achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Open Space Segmentation using Automotive Radar


    Beteiligte:


    Erscheinungsdatum :

    23.11.2020


    Format / Umfang :

    571562 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Open Space Segmentation using Automotive Radar

    Nowruzi, Farzan Erlik | DataCite | 2024


    PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain

    Nowruzi, Farzan Erlik / Kolhatkar, Dhanvin / Kapoor, Prince et al. | TIBKAT | 2021


    Deep Instance Segmentation With Automotive Radar Detection Points

    Liu, Jianan / Xiong, Weiyi / Bai, Liping et al. | IEEE | 2023


    SEMANTIC SEGMENTATION ON AUTOMOTIVE RADAR MAPS

    Prophet, Robert / Li, Gang / Sturm, Christian et al. | British Library Conference Proceedings | 2019


    Semantic Segmentation on Automotive Radar Maps

    Prophet, Robert / Li, Gang / Sturm, Christian et al. | IEEE | 2019