Selecting the neighbourhood size, shape, weights and model order in optical flow estimation

Abstract

Local methods have long been used to estimate optical flow by fitting measurements in a small neighbourhood to a simple model. What is less well known are procedures to choose the neighbourhood size, weights and model order. In this paper, we show that the choice of these local model tuning variables can have a significant effect on the flow estimate. The optimal choice of these variables will depend on the image content, the noise level and the type of motion in the sequence. Hence, the development of a data-driven selection method is important research goal. This paper presents such a procedure based on Stein's unbiased risk estimators (SURE).