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Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm

An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose wea...

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Detalles Bibliográficos
Autores principales: Wang, Li Jia, Zhang, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710940/
https://www.ncbi.nlm.nih.gov/pubmed/26843855
http://dx.doi.org/10.1155/2016/3472184
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author Wang, Li Jia
Zhang, Hua
author_facet Wang, Li Jia
Zhang, Hua
author_sort Wang, Li Jia
collection PubMed
description An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.
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spelling pubmed-47109402016-02-03 Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm Wang, Li Jia Zhang, Hua Comput Intell Neurosci Research Article An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes. Hindawi Publishing Corporation 2016 2015-12-30 /pmc/articles/PMC4710940/ /pubmed/26843855 http://dx.doi.org/10.1155/2016/3472184 Text en Copyright © 2016 L. J. Wang and H. Zhang. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Li Jia
Zhang, Hua
Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
title Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
title_full Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
title_fullStr Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
title_full_unstemmed Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
title_short Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm
title_sort visual tracking based on an improved online multiple instance learning algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710940/
https://www.ncbi.nlm.nih.gov/pubmed/26843855
http://dx.doi.org/10.1155/2016/3472184
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