Extraction of Vehicle Groups in Airborne Lidar Point Clouds With Two-Level Point Processes

In this paper, we present a new object-based hierarchical model for the joint probabilistic extraction of vehicles and groups of corresponding vehicles-called traffic segments-in airborne light detection and ranging (Lidar) point clouds collected from dense urban areas. First, the 3-D point set is classified into terrain, vehicle, roof, vegetation, and clutter classes. Then, the points with the corresponding class labels and echo strength (i.e., intensity) values are projected to the ground. In the obtained 2-D class and intensity maps, we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size, and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical two-level marked point process (MPP) (L
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MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete-return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated ground-truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L
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MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single-layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level, and 95% at group level.