Nowadays, understanding the traffic statistics in real city-scale camera networks takes an important place in the intelligent transportation field. Recently, vehicle route understanding brings a new challenge to the area. It aims to measure the traffic density by identifying the route of each vehicle in traffic cameras. This year, the AI City Challenge holds a competition with real-world traffic data on vehicle route understanding, which requires both efficiency and effectiveness. In this work, we propose Zero-VIRUS, a Zeroshot VehIcle Route Understanding System, which requires no annotation for vehicle tracklets and is applicable for the changeable real-world traffic scenarios. It adopts a novel 2D field modeling of pre-defined routes to estimate the proximity and completeness of each track. The proposed system has achieved third place on Dataset A in stage 1 of the competition (Track 1: Vehicle Counts by Class at Multiple Intersections) against world-wide participants on both effectiveness and efficiency, with a record of the top place on 50% of the test set.


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    Titel :

    Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation


    Beteiligte:
    Yu, Lijun (Autor:in) / Feng, Qianyu (Autor:in) / Qian, Yijun (Autor:in) / Liu, Wenhe (Autor:in) / Hauptmann, Alexander G. (Autor:in)


    Erscheinungsdatum :

    2020-06-01


    Format / Umfang :

    2720876 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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