Detection of Cars in Mobile Lidar Point Clouds

Abstract

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%.