| |
Abstract |
This paper summarizes a system, and its component algorithms, for context-driven target vehicle detection in 3-D data that was developed under the Defense Advanced Research Projects Agency (DARPA) Exploitation of 3-D Data (E3D) Program. In order to determine the power of shape and geometry for the extraction of context objects and the detection of targets, our algorithm research and development concentrated on the geometric aspects of the problem and did not utilize intensity information. Processing begins with extraction of context information and initial target detection at reduced resolution, followed by a detailed, full-resolution analysis of candidate targets. Our reduced-resolution processing includes a probabilistic procedure for finding the ground that is effective even in rough terrain; a hierarchical, graph-based approach for the extraction of context objects and potential vehicle hide sites; and a target detection process that is driven by context-object and hide-site locations. Full-resolution processing includes statistical false alarm reduction and decoy mitigation. When results are available from previously collected data, we also perform object-level change detection, which affects the probabilities that objects are context objects or targets. Results are presented for both synthetic and collected LADAR data. |
|
