A novel multi-modal scene segmentation algorithm for obstacle identification and masking is presented in this work. A co-registered data set is generated from monocular camera and light detection and ranging (LIDAR) sensors. This calibrated data enables 3D scene information to be mapped to time-synchronized 2D camera images, where discontinuities in the ranging data indicate the increased likelihood of obstacle edges. Applications include Advanced Driver Assistance Systems (ADAS) which address lane-departure, pedestrian protection and collision avoidance and require both high-quality image segmentation and computational efficiency. Simulated and experimental results that demonstrate system performance are presented.


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    Titel :

    Multi-Modal Image Segmentation for Obstacle Detection and Masking


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Lee, Cheng-Lung (Autor:in) / Smalley, Christopher (Autor:in) / Nguyen, Hong (Autor:in) / Zhang, Hongyi (Autor:in) / Paulik, Mark J. (Autor:in) / Mohammad, Utayba (Autor:in) / Wu, Yu-Ting (Autor:in)

    Kongress:

    SAE 2014 World Congress & Exhibition ; 2014



    Erscheinungsdatum :

    2014-04-01




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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