We present a system capable of detecting cars in gray-valued videos of traffic scenes based on easy-to-compute orientation selective features derived from gradient filter outputs. The car detection system consists of two processing stages (initial detection and confirmation) and is embedded into a comprehensive architecture of interacting modules optimized for various aspects of driver assistance applications. The initial detection stage uses a heuristic for generating hypotheses which are then presented to a single neural network (NN) classifier for confirmation, which is trained on examples in a supervised way. We show that one can achieve approximate scale-invariance in the confirmation stage by using approximately scale-invariant image features and training with differently sized examples. .The NN used for confirmation are optimized using a simple pruning algorithm. The dependence of detection accuracy and network complexity is investigated; we find that extremely simple networks give surprisingly good classification accuracies at very high speed.


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    Title :

    Real-time detection and classification of cars in video sequences


    Contributors:
    Gepperth, A. (author) / Edelbrunner, J. (author) / Bucher, T. (author)


    Publication date :

    2005-01-01


    Size :

    1296865 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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