Investigating Optical Flow Computation: From Local Methods to a Multiresolution Horn-Schunck Implementation with Bilinear Interpolation

TL;DR

This paper analyzes local and global optical flow methods, introduces a multiresolution Horn-Schunck implementation with bilinear interpolation, and evaluates their effectiveness in motion estimation under different image conditions.

Contribution

It presents a novel multiresolution Horn-Schunck algorithm with bilinear interpolation, enhancing accuracy and convergence in optical flow estimation.

Findings

Multiresolution Horn-Schunck improves motion estimation accuracy.

Bilinear interpolation enhances convergence speed.

Combined strategies are effective under varying image conditions.

Abstract

This paper presents an applied analysis of local and global methods, with a focus on the Horn-Schunck algorithm for optical flow computation. We explore the theoretical and practical aspects of local approaches, such as the Lucas-Kanade method, and global techniques such as Horn-Schunck. Additionally, we implement a multiresolution version of the Horn-Schunck algorithm, using bilinear interpolation and prolongation to improve accuracy and convergence. The study investigates the effectiveness of these combined strategies in estimating motion between frames, particularly under varying image conditions.