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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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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. |
format | Online Article Text |
id | pubmed-4710940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wanglijia visualtrackingbasedonanimprovedonlinemultipleinstancelearningalgorithm AT zhanghua visualtrackingbasedonanimprovedonlinemultipleinstancelearningalgorithm |