Night time vehicle detection and tracking has been a challenging task in recent years. This paper presents a novel context-aware traffic surveillance system that integrates sensor information from autonomous vehicles to improve performance of night time vehicle detection and tracking. The key elements of the proposed method include a novel vehicle pairing framework that represents vehicles based on the fused sensor contexts and vehicle taillights. These detected vehicles are then tracked in real-time night time traffic videos. Experiments are conducted on real traffic videos and the proposed system attains 0.6319 in multiple object tracking accuracy (MOTA), which represents a 26.1% increase compared with the baseline performance.


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    Titel :

    Night Time Vehicle Detection and Tracking by Fusing Sensor Cues from Autonomous Vehicles


    Beteiligte:
    Zhang, Xinxiang (Autor:in) / Story, Brett (Autor:in) / Rajan, Dinesh (Autor:in)


    Erscheinungsdatum :

    01.05.2020


    Format / Umfang :

    203213 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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