Improvements in sensor technologies as well as machine learning methods allow an efficient collection, processing and analysis of the dynamic environment, which can be used for detection and tracking of traffic participants. Current datasets in this domain mostly present a single view, making highly accurate pose estimation impossible due to occlusions. The integration of different, simultaneously acquired data allows to exploit and develop collaboration principles to increase the quality, reliability and integrity of the derived information. This work addresses this problem by providing a multi-view dataset, including 2D image information (videos) obtained by up to three cameras and 3D point clouds from up to five LiDAR sensors together with labels of the traffic participants in the scene. The measurements were conducted during different weather conditions on several days at a large junction in Hanover, Germany, resulting in a total duration of 145 minutes.


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    Title :

    LUMPI: The Leibniz University Multi-Perspective Intersection Dataset


    Contributors:


    Publication date :

    2022-06-05


    Size :

    2050495 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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