It is important to understand the factors underlying grade-crossing crashes and to examine potential solutions. We have installed a camera in front of a locomotive to examine grade-crossing accidents (or near accidents). We present a computer vision system that automatically extracts possible near-accident scenes by detecting the activity of vehicles crossing in front of the train after signals are ignited. We present a fast algorithm to detect moving objects recorded by a moving camera with minimal computation. The moving object is detected by: 1) estimating the ego motion of the camera and 2) detecting and tracking feature points whose motion is inconsistent with the camera motion. We introduce a pseudoreal-time ego-motion (camera-motion) estimation method with a robust optimization algorithm. We present experiments on ego-motion estimation and moving-object detection. Our algorithm works in pseudoreal-time and we expect that our algorithm can be applied to real-time applications such as collision warning in the near future, with the development of hardware technology.


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    Titel :

    Pseudoreal-time activity detection for railroad grade-crossing safety


    Beteiligte:
    ZuWhan Kim, (Autor:in) / Cohn, T.E. (Autor:in)


    Erscheinungsdatum :

    2004-12-01


    Format / Umfang :

    1260430 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Pseudoreal-Time Activity Detection for Railroad Grade-Crossing Safety

    Kim, Z. / Cohn, T. E. / IEEE | British Library Conference Proceedings | 2004




    Pseudo-Realtime Activity Detection for Railroad Grade Crossing Safety

    Kim, Z. / Cohn, T. E. / IEEE | British Library Conference Proceedings | 2003