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Open AccessArticle

An Empirical Study of Exhaustive Matching for Improving Motion Field Estimation

by Vanel Lazcano
Núcleo de Matemática, Física y Estadística, Facultad de Estudios Interdisciplinarios, Universidad Mayor, Santiago 7500628, Chile
Current address: Avda. Manuel Montt 318, Providencia, Santiago 7500628, Chile.
Information 2018, 9(12), 320;
Received: 20 October 2018 / Revised: 6 December 2018 / Accepted: 7 December 2018 / Published: 12 December 2018
(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))
Optical flow is defined as the motion field of pixels between two consecutive images. Traditionally, in order to estimate pixel motion field (or optical flow), an energy model is proposed. This energy model is composed of (i) a data term and (ii) a regularization term. The data term is an optical flow error estimation and the regularization term imposes spatial smoothness. Traditional variational models use a linearization in the data term. This linearized version of data term fails when the displacement of the object is larger than its own size. Recently, the precision of the optical flow method has been increased due to the use of additional information, obtained from correspondences computed between two images obtained by different methods such as SIFT, deep-matching, and exhaustive search. This work presents an empirical study in order to evaluate different strategies for locating exhaustive correspondences improving flow estimation. We considered a different location for matching random locations, uniform locations, and locations on maximum gradient magnitude. Additionally, we tested the combination of large and medium gradients with uniform locations. We evaluated our methodology in the MPI-Sintel database, which represents the state-of-the-art evaluation databases. Our results in MPI-Sintel show that our proposal outperforms classical methods such as Horn-Schunk, TV-L1, and LDOF, and our method performs similar to MDP-Flow. View Full-Text
Keywords: motion estimation; large displacement; color and gradient constancy constraint motion estimation; large displacement; color and gradient constancy constraint
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Lazcano, V. An Empirical Study of Exhaustive Matching for Improving Motion Field Estimation. Information 2018, 9, 320.

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