H. Yalcin, R. Collins, and M. 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),
San Diego, CA, June 2005. *BEST PAPER AWARD*
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.
Click here for
full paper (807337 bytes, pdf file).