Detecting Moving Objects from Long-Range Atmospheric Turbulence Degraded Videos

This paper presents an improved method to detect moving objects from videos distorted by atmospheric turbulence. The method is based on generating an accurate mask from the changing properties of pixel intensities from frame to frame. The background frame is estimated by calculating the median from a sufficient number of input frames. Three different masks are generated by thresholding the difference image and pixel shiftmap of each input frame with respect to the background. A final mask is then obtained by combining all these three masks, which is more accurate than the individual ones. The performance of the proposed method is compared with that of an existing method by applying them on real-world videos. Results show that the proposed method provides better detection of moving objects than the compared method.