A TWO-LEVEL APPROACH FOR THE CROWD-BASED COLLECTION OF VEHICLES FROM 3D POINT CLOUDS

Abstract

In this article, we present a two-level approach for the crowd-based collection of vehicles from 3D point clouds. In the first level, the crowdworkers are asked to identify the coarse positions of vehicles in 2D rasterized shadings that were derived from the 3D point cloud. In order to increase the quality of the results, we utilize the wisdom of the crowd principle which says that averaging multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best estimate. For this, each crowd job is duplicated 10 times and the multiple results are integrated with a DBSCAN cluster algorithm. In the second level, we use the integrated results as pre-information for extracting small subsets of the 3D point cloud that are then presented to crowdworkers for approximating the included vehicle by means of a Minimum Bounding Box (MBB). Again, the crowd jobs are duplicated 10 times and an average bounding box is calculated from the individual bounding boxes. We will discuss the quality of the results of both steps and show that the wisdom of the crowd significantly improves the completeness as well as the geometric quality. With a tenfold acquisition, we have achieve a completeness of 93.3 percent and a geometric deviation of less than 1 m for 95 percent of the collected vehicles.