Object Class Recognition in Mobile Urban Lidar Data Using Global Shape Descriptors

A method is presented to automatically classify objects that lie within the vicinity of streets in 3D point clouds of urban environments. The system first successfully segments objects of interest from the scene through a combination of ground segmentation and road extraction using a Kalman filtering approach, and cluster extraction region growing. Those clusters that fall close to the road are then passed to a classification phase, where they are compared against a labelled database of such clusters. The comparison of clusters is based upon Variable Dimensional Global Shape Descriptors, which encode the geometry of the objects into multi-dimensional histograms, the similarities of which are measured against the database clusters using a variety of metrics including Earth Mover's Distance and Bhattacharya similarity. The method was applied to dense data acquired from central New York City, covering an area of 78, 000 m(2). On a test set containing 101 objects partitioned into 5 classes, the method had an average successful recognition rate of 94.5% for a rich set of vehicles, pedestrians, and street furniture such as fire hydrants, street signs, and poles.