A review on vision-based motion estimation

TL;DR

This paper reviews vision-based motion estimation methods, discusses their advantages and limitations, and introduces a Gaussian kernel-based method that improves accuracy on simple images.

Contribution

It provides a comprehensive review of existing methods and proposes a new Gaussian kernel-based approach to enhance accuracy in vision-based motion measurement.

Findings

The Gaussian kernel-based method achieves high accuracy on synthesized images.

Existing methods struggle to balance accuracy and robustness.

The review highlights key advantages and limitations of current techniques.

Abstract

Compared to contact sensors-based motion measurement, vision-based motion measurement has advantages of low cost and high efficiency and have been under active development in the past decades. This paper provides a review on existing motion measurement methods. In addition to the development of each branch of vision-based motion measurement methods, this paper also discussed the advantages and disadvantages of existing methods. Based on this discussion, it was identified that existing methods have a common limitation in optimally balancing accuracy and robustness. To address issue, we developed the Gaussian kernel-based motion measurement method. Preliminary study shows that the developed method can achieve high accuracy on simple synthesized images.