Readjusting Unstable Regions to Improve the Quality of High Accuracy Optical Flow

Optical flow is an important problem in computer vision since applications of accurate optical flow estimation enable us to control and manipulate tracking, 3-D reconstruction, motion blurring, and dirt removal. Many powerful methods have been proposed to solve the optical flow problem; however, instabilities at the boundaries of moving objects are still challenges. A difficult part of the optical flow problem is how to accurately and quickly detect and readjust unstable regions at the boundaries. This paper aims to enhance optical flow estimation by detecting and readjusting the unstable regions. In this paper, a new algorithm to detect and quickly readjust unstable regions at the boundaries of moving objects is presented in a more general and compact manner. In addition, a new context-based anisotropic diffusion filter, which is significant in processing intermediate data, is discussed in detail. Our approach has demonstrated more accurate results than previous approaches.