Proceedings Article | 5 May 2009
KEYWORDS: Detection and tracking algorithms, Roads, 3D image processing, Object recognition, Buildings, Algorithm development, Feature extraction, Databases, Image segmentation, Clouds
Military operations in urban areas often require detailed knowledge of the location and identity of commonly occurring
objects and spatial features. The ability to rapidly acquire and reason over urban scenes is critically important to such
tasks as mission and route planning, visibility prediction, communications simulation, target recognition, and inference
of higher-level form and function. Under DARPA's Urban Reasoning and Geospatial ExploitatioN Technology
(URGENT) Program, the BAE Systems team has developed a system that combines a suite of complementary feature
extraction and matching algorithms with higher-level inference and contextual reasoning to detect, segment, and classify
urban entities of interest in a fully automated fashion. Our system operates solely on colored 3D point clouds, and
considers object categories with a wide range of specificity (fire hydrants, windows, parking lots), scale (street lights,
roads, buildings, forests), and shape (compact shapes, extended regions, terrain). As no single method can recognize the
diverse set of categories under consideration, we have integrated multiple state-of-the-art technologies that couple
hierarchical associative reasoning with robust computer vision and machine learning techniques. Our solution leverages
contextual cues and evidence propagation from features to objects to scenes in order to exploit the combined descriptive
power of 3D shape, appearance, and learned inter-object spatial relationships. The result is a set of tools designed to
significantly enhance the productivity of analysts in exploiting emerging 3D data sources.
