Scale-change aware locally adaptive optical flow

Optical flow is one of the key components in computer vision research area. Since the seminal work proposed by Horn and Schunck [1], numerous advanced algorithms have been proposed. Many state-of-the-art optical flow estimation algorithms optimize the data and regularization terms to solve ill-posed problems. However, despite their major advances over last decade, conventional optical flow methods utilize a single or fixed data terms without concerning scale changes in two consecutive frames of images. In this paper, we propose scale-change aware block matching data terms fused with locally adaptive models to establish dense correspondence between frames containing objects in different scales. We observed that taking scale variations into account in matching has a positive effect on optical flow accuracy.