Automatic Vehicle Extraction from airborne LiDAR data of urban areas using morphological reconstruction

In this paper, we address issues in traffic monitoring of urban areas using airborne LiDAR data. Our aim in this paper is to extract individual vehicles from common LiDAR data of urban areas, based on which the dynamical status of vehicles and other traffic-related parameters can be derived. A contextguiding bottom-up processing strategy is developed to accomplish the task. Ground level separation is first used to exclude the irrelevant objects and provide the Region of Interest. The marker-controlled watershed transformation assisted by morphological reconstruction is then performed on the gridded and filled raster of ground level points to delineate the single vehicles. The evaluation of experimental results has shown that most vehicles can be correctly detected, whose delineated contours are accurate.