Robust tracking in aerial imagery based on an ego-motion Bayesian model
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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In this paper, we propose a method for detecting and tracking small targets in forward looking infrared(FLIR) image sequences taken from an airborne moving platform. Firstly, we adopt the morphological connected operator to remove the undesirable clutter in the background. Secondly, the image is decomposed by morphological Haar wavelet, and the wavelet energy image is computed from the horizontal and vertical detail images, and it is fused with the scaled image. Thirdly, the targets are extracted coarse-to-fine by adaptive double thresholding. Finally, targets are modeled by intensity probabilistic density function and tracked using mean shift algorithm. The experiments performed on the AMCOM FLIR data set verify thevalidity and robustness of the algorithm.