We explore the problem of intersection classification using monocular on-board passive vision, with the goal of classifying traffic scenes with respect to road topology. We divide the existing approaches into two broad categories according to the type of input data: (a) first person vision (FPV) approaches, which use an egocentric view sequence as the intersection is passed; and (b) third person vision (TPV) approaches, which use a single view immediately before entering the intersection. The FPV and TPV approaches each have advantages and disadvantages. Therefore, we aim to combine them into a unified deep learning framework. Experimental results show that the proposed FPV-TPV scheme outperforms previous methods and only requires minimal FPV/TPV measurements.


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    Title :

    Deep Intersection Classification Using First and Third Person Views


    Contributors:


    Publication date :

    2019-06-01


    Size :

    2801505 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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