Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2019

Abstract

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale and rotation changes simultaneously in log-polar coordinates. Moreover, a variant of correlation filter is used to predict the translational motion individually. Our experimental results demonstrate that the proposed tracker achieves very promising prediction performance compared with the state-of-the-art visual object tracking methods while still retaining the advantages of high efficiency and simplicity in conventional correlation filter-based tracking methods.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 33rd AAAI Conference on Artificial Intelligence 2019: Honolulu, January 27 - February 1

First Page

8666

Last Page

8673

Identifier

10.1609/aaai.v33i01.33018666

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

https://doi.org/10.1609/aaai.v33i01.33018666

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