Abstract
Atmospheric turbulence causes blurring and geometrical distortions in images acquired from a long distance. It makes it difficult to detect moving objects due to both the irregular movements and deformations of the pixels. In this study, we propose a fast method to detect moving objects in turbulence-degraded image sequences. It combines an efficient registration and background subtraction techniques. Since we model the image degradation as local linear deformations, it is estimated by the motion patterns calculated by optical flow. We utilize feature based optical flow and incremental reference frame generation in registration stage. After warping the frames using the registration result GMM based background subtraction technique detects moving objects in stabilized frames. The experiments performed on common image sequences show that the proposed method detects moving objects faster than the available methods, without distorting the objects.
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