This paper describes a method for automated detection of temporary cars in Mobile LiDAR point clouds. It consists of a segment-based classification of static cars and a comparison of data from two sensors to identify moving cars. Two segmentation methods are used to extract the ground and group the above-ground points into objects. From each segmented object a number of features are extracted, and a classification strengthened by feature selection is performed to classify temporary cars. We evaluate the performance of two different classifiers trained with a training set including 117 temporary cars, and show classification accuracies of up to 92%. We also investigate a method for identifying moving cars based on the distance between corresponding segments in the point clouds captured by the two scanning sensors, and report an overall accuracy of 61%.


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    Title :

    Detection of Cars in Mobile Lidar Point Clouds


    Contributors:


    Publication date :

    2018-09-01


    Size :

    7184438 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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