In this paper, the authors have presented a night-time detection computer system for driving assistance. On the one hand, the system performance is satisfactory for head lights (detection range up to 300-500 m) but on the other hand, the performance for tail lights (detection range up to 50-80 m) must be improved. One advantage of the system is that works in real-time conditions. The computation time spent on processing one input frame depends on its road scene complexity and the number of blobs. In average the processing frame rate is close to 20fps, which satisfies real-time demands. The classification mistakes between vehicles and road signs, are very low (PD = 0.9458, PFA = 0.0659), and can be improved considering more distinctive invariant features and increasing the size of the training and test data. The results are encouraging, and the authors plan to include several improvements to the current implementation. More work must be done in the classification process in order to increase the accuracy of the classifier. Special attention is devoted in the classifier trying to incorporate new invariant parameters such as the Gaussian curvature or one and second order derivatives. In addition, multi-scale top-hat transformations can help in the halo detection at far distances. In order to achieve a more reliable distance estimation, some parameters such as the blob size, distance between blobs and the relative growth/shrinking in the the image can be fused to obtain a better estimation. As a future idea, a red filter is going to be introduced into the system in order to increase tail lights detection range up to 400 m.


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    Title :

    Automatic LightBeam Controller for driver assistance


    Additional title:

    Ein automatischer Lichtstrahl-Controller zur Fahrerassistenz


    Contributors:
    Alcantarilla, P.F. (author) / Bergasa, L.M. (author) / Jimenez, P. (author) / Parra, I. (author) / Llorca, D.F. (author) / Sotelo, M.A. (author) / Mayoral, S.S. (author)

    Published in:

    Publication date :

    2011


    Size :

    17 Seiten, 24 Bilder, 6 Tabellen, 23 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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