Vehicle Activity Indication from Airborne Lidar Data of Urban Areas by Binary Shape Classification of Point Sets

This paper presents a generic scheme to analyze urban traffic via vehicle motion indication from airborne laser scanning (ALS) data. The scheme comprises two main steps performed progressively vehicle extraction and motion status classification. The step for vehicle extraction is intended to detect and delineate single vehicle instances as accurate and complete as possible, while the step for motion status classification takes advantage of shape artefacts defined for moving vehicle model, to classify the extracted vehicle point sets based on parameterized boundary features, which are sufficiently good to describe the vehicle shape. To accomplish the tasks, a hybrid strategy integrating context-guided method with 3-d segmentation based approach is applied for vehicle extraction. Then, a binary classification method using Lie group based distance is adopted to determine the vehicle motion status. However, the vehicle velocity cannot be derived at this stage due to unknown true size of vehicle. We illustrate the vehicle motion indication scheme by two examples of real data and summarize the performance by accessing the results with respect to reference data manually acquired, through which the feasibility and high potential of airborne LiDAR for urban traffic analysis are verified. * Corresponding author.