An autonomous vehicle has to be able to perceive and understand its environment. At perception level objects are detected and classified using raw sensory data, while at situation interpretation level high-level object knowledge, like object relations, is required. In order to make a step towards bridging this gap between low-level perception and scene understanding the authors combine computer vision models with the probabilistic logic formalism Markov logic. The proposed approach allows for joint inference of object relations between all object pairs observed in a traffic scene, explicitly taking into account the scene context. Experimental results based on simulated data as well as on automatically segmented traffic videos from an on-board stereo camera platform are provided.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Understanding object relations in traffic scenes


    Beteiligte:
    Hensel, Irina (Autor:in) / Bachmann, Alexander (Autor:in) / Hummel, Britta (Autor:in) / Tran, Quan (Autor:in)


    Erscheinungsdatum :

    2010


    Format / Umfang :

    7 Seiten, 15 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Understanding Pedestrian Behavior in Complex Traffic Scenes

    Rasouli, Amir / Kotseruba, Iuliia / Tsotsos, John K. | IEEE | 2018


    Understanding Pedestrians’ Car-Hailing Intention in Traffic Scenes

    Wang, Zhenghao / Lian, Jing / Li, Linhui et al. | Springer Verlag | 2022


    Semantic Understanding of Traffic Scenes with Large Vision Language Models

    Jain, Sandesh / Thapa, Surendrabikram / Chen, Kuan-Ting et al. | IEEE | 2024


    Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes

    Wu, Y. / Zhang, D. / Zhang, L. et al. | BASE | 2022

    Freier Zugriff

    A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes

    Oeljeklaus, Malte / Hoffmann, Frank / Bertram, Torsten | IEEE | 2018