Recursive Optical Flow Estimation—Adaptive Filtering Approach

Abstract

This paper presents a new approach based on the differential framework proposed by Horn and Schunck, to the problem of recursive optical flow estimation from image sequences. The original method of Horn and Schunck is applicable only to the problem of estimating the optical flow between a pair of images from an image sequence. When we aim at estimating the optical flow for long image sequences recursively, the question is whether and how can we gain from previous estimates. In this paper we show that gain is achieved from both computational and accuracy points of view. Incorporation of the time axis into the estimation process is done by assuming temporal smoothness of the optical flow, resulting in simplified spatial–temporal models. The obtained models permit incorporation of the constrained weighted least squares (CWLS) estimator. This estimator is shown to yield RLS and LMS adaptive filter versions for recursive optical flow estimation in time. An interesting and desirable property of the proposed estimation algorithms is their flexibility with respect to performance versus computational requirements. By a simple choice of a parameter these algorithms can be modified to exploit the available time to improve their performance with respect to estimation error. The convergence properties of these estimation algorithms are analyzed. Simulations for various image sequences support the analysis and demonstrate the performance of the estimation algorithms.