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Single object tracking using particle filter framework and saliency-based weighted color histogram

Single object tracking using particle filter framework and saliency-based weighted color histogram Despite many years of research, object tracking remains a challenging problem, not only because of the variety of object appearances, but also because of the complexity of surrounding environments. In this research, we present an algorithm for single object tracking using a particle filter framework and color histograms. Particle filters are iterative algorithms that perform predictions in each iteration using particles, which are samples drawn from a statistical distribution. Color histograms are embedded in these particles, and the distances between histograms are used to measure likelihood between targets and observations. One downside of color histograms is that they ignore spatial information, which may produce tracking failure when objects appear that are similar in color. To overcome this disadvantage, we propose a saliency-based weighting scheme for histogram calculation. Given an image region, first its saliency map is generated. Next, its histogram is calculated based on the generated saliency map. Pixels located in salient regions have higher weights than those in others, which helps preserve the spatial information. Experimental results showed the efficiency of the proposed appearance model in object tracking under various conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Single object tracking using particle filter framework and saliency-based weighted color histogram

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
DOI
10.1007/s11042-018-6180-5
Publisher site
See Article on Publisher Site

Abstract

Despite many years of research, object tracking remains a challenging problem, not only because of the variety of object appearances, but also because of the complexity of surrounding environments. In this research, we present an algorithm for single object tracking using a particle filter framework and color histograms. Particle filters are iterative algorithms that perform predictions in each iteration using particles, which are samples drawn from a statistical distribution. Color histograms are embedded in these particles, and the distances between histograms are used to measure likelihood between targets and observations. One downside of color histograms is that they ignore spatial information, which may produce tracking failure when objects appear that are similar in color. To overcome this disadvantage, we propose a saliency-based weighting scheme for histogram calculation. Given an image region, first its saliency map is generated. Next, its histogram is calculated based on the generated saliency map. Pixels located in salient regions have higher weights than those in others, which helps preserve the spatial information. Experimental results showed the efficiency of the proposed appearance model in object tracking under various conditions.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 4, 2018

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