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Background Estimation under Rapid Gain Change in Thermal Imagery
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Robert Collins
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Martial Hebert
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We consider detection of moving ground vehicles in airborne sequences recorded by a thermal sensor with automatic gain control, using an approach that integrates dense optic flow over time to maintain a model of background appearance and a foreground occlusion layer mask. However, the automatic gain control of the thermal sensor introduces rapid changes in intensity that makes this difficult. In this paper we show that an intensity-clipped affine model of sensor gain is sufficient to describe the behavior of our thermal sensor. We develop a method for gain estimation and compensation that uses sparse flow of corner features to compute the affine back- ground scene motion that brings pairs of frames into alignment prior to estimating change in pixel brightness. Dense optic flow and background appearance modeling is then performed on these motion-compensated and brightness-compensated frames. Experimental results demonstrate that the resulting algorithm can segment ground vehicles from thermal airborne video while building a mosaic of the background layer, despite the presence of rapid gain changes. |
| Related Publications |
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Second IEEE Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS'05) in conjunction with CVPR’05, San Diego, CA, 2005. [Abstract] [pdf] [Best paper award,  ]
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Carnegie Mellon University, Robotics Institute
5000 Forbes Av., Pittsburgh, PA, 15213
hulyayalcin@gmail.com
