Multi-Mask Based Stabilization of Turbulence Degraded Videos Containing Moving Objects

Stabilizing videos and detecting moving objects are important tasks in many computer vision applications, though it becomes challenging because of the presence of atmospheric turbulence that causes random pixel shifting and blurring of the videos. This paper proposes an improved method for correcting geometrical distortions of videos degraded by atmospheric turbulence while keeping moving objects unaltered. In this method, three different techniques are used to generate three different masks, which are then combined together to generate a more accurate mask. This mask is employed to properly detect the moving objects and finally fusing with the background a stabilized video output is obtained. The performance of this method is tested by applying it on different real-world datasets. A comparison with an existing method shows that the proposed method gives better detection of moving objects and improved stabilization of the degraded videos.