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Pixel-Guided Association for Multi-Object Tracking
Propagation and association tasks in Multi-Object Tracking (MOT) play a pivotal role in accurately linking the trajectories of moving objects. Recently, modern deep learning models have been addressing these tasks by introducing fragmented solutions for each different problem such as appearance mode...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692782/ https://www.ncbi.nlm.nih.gov/pubmed/36433519 http://dx.doi.org/10.3390/s22228922 |
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author | Boragule, Abhijeet Jang, Hyunsung Ha, Namkoo Jeon, Moongu |
author_facet | Boragule, Abhijeet Jang, Hyunsung Ha, Namkoo Jeon, Moongu |
author_sort | Boragule, Abhijeet |
collection | PubMed |
description | Propagation and association tasks in Multi-Object Tracking (MOT) play a pivotal role in accurately linking the trajectories of moving objects. Recently, modern deep learning models have been addressing these tasks by introducing fragmented solutions for each different problem such as appearance modeling, motion modeling, and object associations. To bring unification in the MOT task, we introduce a pixel-guided approach to efficiently build the joint-detection and tracking framework for multi-object tracking. Specifically, the up-sampled multi-scale features from consecutive frames are queued to detect the object locations by using a transformer–decoder, and per-pixel distributions are utilized to compute the association matrix according to object queries. Additionally, we introduce a long-term appearance association on track features to learn the long-term association of tracks against detections to compute the similarity matrix. Finally, a similarity matrix is jointly integrated with the Byte-Tracker resulting in a state-of-the-art MOT performance. The experiments with the standard MOT15 and MOT17 benchmarks show that our approach achieves significant tracking performance. |
format | Online Article Text |
id | pubmed-9692782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96927822022-11-26 Pixel-Guided Association for Multi-Object Tracking Boragule, Abhijeet Jang, Hyunsung Ha, Namkoo Jeon, Moongu Sensors (Basel) Article Propagation and association tasks in Multi-Object Tracking (MOT) play a pivotal role in accurately linking the trajectories of moving objects. Recently, modern deep learning models have been addressing these tasks by introducing fragmented solutions for each different problem such as appearance modeling, motion modeling, and object associations. To bring unification in the MOT task, we introduce a pixel-guided approach to efficiently build the joint-detection and tracking framework for multi-object tracking. Specifically, the up-sampled multi-scale features from consecutive frames are queued to detect the object locations by using a transformer–decoder, and per-pixel distributions are utilized to compute the association matrix according to object queries. Additionally, we introduce a long-term appearance association on track features to learn the long-term association of tracks against detections to compute the similarity matrix. Finally, a similarity matrix is jointly integrated with the Byte-Tracker resulting in a state-of-the-art MOT performance. The experiments with the standard MOT15 and MOT17 benchmarks show that our approach achieves significant tracking performance. MDPI 2022-11-18 /pmc/articles/PMC9692782/ /pubmed/36433519 http://dx.doi.org/10.3390/s22228922 Text en © 2022 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 Boragule, Abhijeet Jang, Hyunsung Ha, Namkoo Jeon, Moongu Pixel-Guided Association for Multi-Object Tracking |
title | Pixel-Guided Association for Multi-Object Tracking |
title_full | Pixel-Guided Association for Multi-Object Tracking |
title_fullStr | Pixel-Guided Association for Multi-Object Tracking |
title_full_unstemmed | Pixel-Guided Association for Multi-Object Tracking |
title_short | Pixel-Guided Association for Multi-Object Tracking |
title_sort | pixel-guided association for multi-object tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692782/ https://www.ncbi.nlm.nih.gov/pubmed/36433519 http://dx.doi.org/10.3390/s22228922 |
work_keys_str_mv | AT boraguleabhijeet pixelguidedassociationformultiobjecttracking AT janghyunsung pixelguidedassociationformultiobjecttracking AT hanamkoo pixelguidedassociationformultiobjecttracking AT jeonmoongu pixelguidedassociationformultiobjecttracking |