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Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper...

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Detalles Bibliográficos
Autores principales: Mehmood, Khizer, Jalil, Abdul, Ali, Ahmad, Khan, Baber, Murad, Maria, Cheema, Khalid Mehmood, Milyani, Ahmad H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073341/
https://www.ncbi.nlm.nih.gov/pubmed/33920648
http://dx.doi.org/10.3390/s21082841
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author Mehmood, Khizer
Jalil, Abdul
Ali, Ahmad
Khan, Baber
Murad, Maria
Cheema, Khalid Mehmood
Milyani, Ahmad H.
author_facet Mehmood, Khizer
Jalil, Abdul
Ali, Ahmad
Khan, Baber
Murad, Maria
Cheema, Khalid Mehmood
Milyani, Ahmad H.
author_sort Mehmood, Khizer
collection PubMed
description Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.
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spelling pubmed-80733412021-04-27 Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking Mehmood, Khizer Jalil, Abdul Ali, Ahmad Khan, Baber Murad, Maria Cheema, Khalid Mehmood Milyani, Ahmad H. Sensors (Basel) Article Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis. MDPI 2021-04-17 /pmc/articles/PMC8073341/ /pubmed/33920648 http://dx.doi.org/10.3390/s21082841 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mehmood, Khizer
Jalil, Abdul
Ali, Ahmad
Khan, Baber
Murad, Maria
Cheema, Khalid Mehmood
Milyani, Ahmad H.
Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
title Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
title_full Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
title_fullStr Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
title_full_unstemmed Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
title_short Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking
title_sort spatio-temporal context, correlation filter and measurement estimation collaboration based visual object tracking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073341/
https://www.ncbi.nlm.nih.gov/pubmed/33920648
http://dx.doi.org/10.3390/s21082841
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