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Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence

Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges n...

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Detalles Bibliográficos
Autor principal: Wang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042603/
https://www.ncbi.nlm.nih.gov/pubmed/35498174
http://dx.doi.org/10.1155/2022/1550543
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author Wang, Yu
author_facet Wang, Yu
author_sort Wang, Yu
collection PubMed
description Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges need to be solved. The rapid development of deep learning has promoted the research progress of digital vision. Target tracking technology is an important foundation of digital vision research, which makes it develop from academia to industry. In this paper, a method of target tracking using MDNet is introduced. Starting with the attention mechanism, two attention mechanisms are added to extract and integrate the better features. Case partitioning is used to reduce the investment of tracking module and minimize the network size during tracking, and its result can be prevented from getting worse. Finally, the experiment is analyzed in detail.
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spelling pubmed-90426032022-04-27 Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence Wang, Yu Comput Intell Neurosci Research Article Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges need to be solved. The rapid development of deep learning has promoted the research progress of digital vision. Target tracking technology is an important foundation of digital vision research, which makes it develop from academia to industry. In this paper, a method of target tracking using MDNet is introduced. Starting with the attention mechanism, two attention mechanisms are added to extract and integrate the better features. Case partitioning is used to reduce the investment of tracking module and minimize the network size during tracking, and its result can be prevented from getting worse. Finally, the experiment is analyzed in detail. Hindawi 2022-04-19 /pmc/articles/PMC9042603/ /pubmed/35498174 http://dx.doi.org/10.1155/2022/1550543 Text en Copyright © 2022 Yu Wang. 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, Yu
Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
title Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
title_full Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
title_fullStr Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
title_full_unstemmed Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
title_short Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
title_sort research on intelligent target tracking algorithm based on mdnet under artificial intelligence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042603/
https://www.ncbi.nlm.nih.gov/pubmed/35498174
http://dx.doi.org/10.1155/2022/1550543
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