An Efficient Mask Generation Method for Moving Object Detection in Atmospheric Imaging

Atmospheric turbulence causes non-uniform geometric deformation of images due to random fluctuation of refractive index throughout the imaging path. The identification of moving objects in a turbulent medium is a fundamental preprocessing step in computer vision. In this paper, a new mask generation process is proposed that removes misdetection due to turbulence in the medium. In this regard, the first step is to estimate the background frame from the video and determine the difference images from the input frames with respect to the background frame. Then three different thresholding techniques: Otsu thresholding, maximum entropy based thresholding, and standard deviation and mean based thresholding, are applied on these difference images to generate three different masks. Finally, a refined mask is generated using these three masks which detects the moving objects from the degraded video. In simulation experiment, qualitative comparisons are conducted to evaluate the performance of the proposed method with a previous one and higher accuracy is obtained.