This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian-sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.


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    Title :

    A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking


    Contributors:


    Publication date :

    2007-09-01


    Size :

    860696 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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