In this paper, we propose a novel deep learning architecture to classify unsafe driving maneuvers from dashcam and IMU data. Such architecture processes the output of an object detection algorithm in combination with raw video frames and GPS/IMU data. At the core of the architecture there is a novel Spatio-Temporal Attention Selector (STAS) module, which (1) extracts features describing the evolution of each object in the scene over time and (2) leverages multi-head dot product attention to select the relevant ones, i.e., the dangerous ones or the ones in danger, to perform classification. We also introduce a simple but effective methodology to increase the benefit of fine-tuning the backbone network. Our method is shown to achieve higher performance than other approaches in the literature applying attention over single frames.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Unsafe Maneuver Classification From Dashcam Video and GPS/IMU Sensors Using Spatio-Temporal Attention Selector


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    3851977 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Two-stream neural architecture for unsafe maneuvers classification from dashcam videos and GPS/IMU sensors

    Simoncini, Matteo / de Andrade, Douglas Coimbra / Salti, Samuele et al. | IEEE | 2020


    Classifying Ego-Vehicle Road Maneuvers from Dashcam Video

    Zekany, Stephen A. / Dreslinski, Ronald G. / Wenisch, Thomas F. | IEEE | 2019


    Analysis of Dashcam Video for Determination of Vehicle Speed

    Marquez, Alvaro / Leifer, Jack | SAE Technical Papers | 2020


    POLICE DASHCAM

    CHEN CHI-HSIU | Europäisches Patentamt | 2024

    Freier Zugriff

    Analysis of Dashcam Video for Determination of Vehicle Speed

    Leifer, Jack / Marquez, Alvaro | British Library Conference Proceedings | 2020