Contextual object understanding through geospatial analysis and reasoning (COUGAR)

Joel Douglas; Matthew Antone; James Coggins; Bradley J. Rhodes; Erik Sobel; Frank Stolle; Lori Vinciguerra; Majid Zandipour; Yu Zhong

doi:10.1117/12.823438

4 May 2009 Contextual object understanding through geospatial analysis and reasoning (COUGAR)

Joel Douglas, Matthew Antone, James Coggins, Bradley J. Rhodes, Erik Sobel, Frank Stolle, Lori Vinciguerra, Majid Zandipour, Yu Zhong

Author Affiliations +

Proceedings Volume 7335, Automatic Target Recognition XIX; 733506 (2009) https://doi.org/10.1117/12.823438
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Joel Douglas, Matthew Antone, James Coggins, Bradley J. Rhodes, Erik Sobel, Frank Stolle, Lori Vinciguerra, Majid Zandipour, and Yu Zhong "Contextual object understanding through geospatial analysis and reasoning (COUGAR)", Proc. SPIE 7335, Automatic Target Recognition XIX, 733506 (4 May 2009); https://doi.org/10.1117/12.823438

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