Common strategies for detection and tracking of aerial moving targets in airborne Forward-Looking Infrared
(FLIR) images offer accurate results in images composed by a non-textured sky. However, when cloud and
earth regions appear in the image sequence, those strategies result in an over-detection that increases very
significantly the false alarm rate. Besides, the airborne camera induces a global motion in the image sequence
that complicates even more detection and tracking tasks. In this work, an automatic detection and tracking
system with an innovative and efficient target trajectory filtering is presented. It robustly compensates the
global motion to accurately detect and track potential aerial targets. Their trajectories are analyzed by a curve
fitting technique to reliably validate real targets. This strategy allows to filter false targets with stationary or
erratic trajectories. The proposed system makes special emphasis in the use of low complexity video analysis
techniques to achieve real-time operation. Experimental results using real FLIR sequences show a dramatic
reduction of the false alarm rate, while maintaining the detection rate