Localizing occluded object is a long-term challenge in Advanced Driving Assistant System (ADAS) and autonomous driving research. In this paper, we propose a novel graph-matching based approach that leverages the challenge by adopting the deep learning and multiple-view geometry analysis. Specifically, the 3D scene reconstruction is firstly built by associating the comprehensive graph representations of the multiple-view observations, incorporated with the spatial relationship of the co-visible objects so as their discriminant appearance features. Followed by, the localization for occluded object is achieved by inferring from the reconstructed 3D geometry. We conduct experiments to validate the system in connected vehicular platform in the advanced traffic simulation dataset. The experimental results convincingly indicate the effectiveness of the proposed system in real- time object detection, graph generation, matching and location inference for occluded objects.


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    Titel :

    Collaborative Localization for Occluded Objects in Connected Vehicular Platform


    Beteiligte:
    Guo, Rui (Autor:in) / Lu, Hongsheng (Autor:in) / Gao, Peng (Autor:in) / Zhang, Ziling (Autor:in) / Zhang, Hao (Autor:in)


    Erscheinungsdatum :

    2019-09-01


    Format / Umfang :

    3020686 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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