Adaptive mixed norm optical flow estimation

TL;DR

This paper introduces an adaptive mixed norm method for optical flow estimation that robustly handles noise, outliers, and motion discontinuities without prior noise distribution knowledge, improving motion vector accuracy.

Contribution

It proposes a novel adaptive regularized framework using mixed norm functional with kurtosis-based regularization, enhancing robustness in optical flow estimation.

Findings

Provides robust optical flow estimates in noisy conditions

Handles outliers, discontinuities, and occlusions effectively

No prior noise distribution knowledge required

Abstract

The pel-recursive computation of 2-D optical flow has been extensively studied in computer vision to estimate motion from image sequences, but it still raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. It relies on spatio-temporal brightness variations due to motion. Our proposed adaptive regularized approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Mixed Norm (MN) to estimate the best motion vector for a given pixel. In our model, various types of noise can be handled, representing different sources of error. The motion vector estimation takes into consideration local image properties and it results from the minimization of a mixed norm functional with a regularization parameter depending on the kurtosis. This parameter determines the relative importance of the fourth norm and makes the functional convex. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Experiments indicate that this approach provides robust estimates of the optical flow.