A review on vision-based motion estimation
This is an incremental contribution to the field of computer vision, addressing a specific problem in motion measurement for researchers and practitioners.
The paper reviews vision-based motion estimation methods, identifying a common limitation in balancing accuracy and robustness, and proposes a Gaussian kernel-based method that achieves high accuracy on simple synthesized images.
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.