Precise positioning surveillance in 3-D using night-vision stereoscopic photogrammetry

A 3-D imaging technique is presented which pairs high-resolution night-vision cameras with GPS to increase the capabilities of passive imaging surveillance. Camera models and GPS are used to derive a registered point cloud from multiple night-vision images. These point clouds are used to generate 3-D scene models and extract real-world positions of mission critical objects. Analysis shows accuracies rivaling laser scanning even in near-total darkness. The technique has been tested on stereoscopic 3-D video collections as well. Because this technique does not rely on active laser emissions it is more portable, less complex, less costly, and less detectable than laser scanning. This study investigates close-range photogrammetry under night-vision lighting conditions using practical use-case examples of terrain modeling, covert facility surveillance, and stand-off facial recognition.