In this paper, we address issues in traffic monitoring of urban areasusing airborne LiDAR data. Our aim in this paper is to extract individualvehicles from common LiDAR data of urban areas, based on which thedynamical status of vehicles and other traffic-related parameterscan be derived. A context-guiding bottom-up processing strategy isdeveloped to accomplish the task. Ground level separation is firstused to exclude the irrelevant objects and provide the ldquoRegionof Interestrdquo. The marker-controlled watershed transformationassisted by morphological reconstruction is then performed on thegridded and filled raster of ground level points to delineate thesingle vehicles. The evaluation of experimental results has shownthat most vehicles can be correctly detected, whose delineated contoursare accurate.     «

In this paper, we address issues in traffic monitoring of urban areasusing airborne LiDAR data. Our aim in this paper is to extract individualvehicles from common LiDAR data of urban areas, based on which thedynamical status of vehicles and other traffic-related parameterscan be derived. A context-guiding bottom-up processing strategy isdeveloped to accomplish the task. Ground level separation is firstused to exclude the irrelevant objects and provide the ldquoRegionof Interestrdquo. The marker-...     »