Background Estimation under Rapid Gain Change in Thermal Imagery

Hulya Yalcin, Robert Collins, and Martial Hebert
Second IEEE Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS'05), July, 2005.


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Abstract
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 background 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.

Keywords
thermal imagery, automatic gain control, background estimation

Notes
Number of pages: 8

Text Reference
Hulya Yalcin, Robert Collins, and Martial Hebert, "Background Estimation under Rapid Gain Change in Thermal Imagery," Second IEEE Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS'05), July, 2005.

BibTeX Reference
@inproceedings{Yalcin_2005_5015,
   author = "Hulya Yalcin and Robert Collins and Martial Hebert",
   title = "Background Estimation under Rapid Gain Change in Thermal Imagery",
   booktitle = "Second IEEE Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum (OTCBVS'05)",
   month = "July",
   year = "2005",
}