<title>Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection</title>

In air-to-ground applications, the detection of target is a difficult problem due to complex background where classical detection algorithms generate a large amount of false alarms. This paper addresses the detection of moving target based on motion compensated sequences. In the presence of noisy image acquisition and motion discontinuities, the estimation of optical flow is reformulated in robust estimation framework. The motion estimation is based on robust optical flow algorithm developed in the pyramidal Markov Random Model framework. We present the results of this detection algorithm on real-world airborne I.R. image sequence.