A two stage multi object tracking algorithm with transformer and attention mechanism

In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the issue of experiencing frequent switching of target identity ID switches (IDs). In response to the issues above, this paper proposes a multi-ob...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 31414 - 14
Hauptverfasser:	Hou, Mingxing, Wu, Yiming, Shi, Hong, Mu, Xiaofang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 26.08.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/705 639/705/117 Accuracy Algorithms Attention mechanism Boxes Deep learning Design Humanities and Social Sciences Localization Multi-object tracking multidisciplinary Neural networks Occlusion Re-identification Safety engineering Science Science (multidisciplinary) Two-stage association You Only Look Once Version 8 Two-stage association Attention mechanism Multi-object tracking Re-identification You Only Look Once Version 8
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the issue of experiencing frequent switching of target identity ID switches (IDs). In response to the issues above, this paper proposes a multi-object tracking model that integrates improved You Only Look Once Version 8 (YOLOv8) and High-Performance Multi-Object Tracking by Tracking Bytes (ByteTrack). The model architecture is based on the paradigm of tracking-by-detection. In the detection stage, we combine the coordinate attention mechanism to propose the Coordinate Attention Spatial Pyramid Pooling - Fast Conv (CASPPFC) module, and combine it with improved Efficient Vision Transformer (EfficientViT) to enhance the YOLOv8 backbone network, effectively reducing false positives and false negatives caused by occlusion. In the first stage of tracking association, we propose the Omni-Scale Network-Coordinate Attention (OSNet-CA) network as the Re-identification (Re-ID) feature extraction method to capture target information more effectively. In the second stage of association, we adopt the Efficient Intersection over Union (EIoU) improvement method to comprehensively consider the positional relationships between targets. The effectiveness of the improved model is validated on the Multi-Object Tracking 2017 (MOT17) and Multi-Object Tracking 2020 (MOT20) datasets. The results indicate that our tracking model achieved 80.5% Multiple Object Tracking Accuracy (MOTA), 79.3% Identification F1 Score (IDF1), and 64.2% Higher Order Tracking Accuracy (HOTA) on the MOT17 test set, and 77.8% MOTA, 76.9% IDF1, and 62.4% HOTA on the MOT20 test set. This tracking model can achieve high-precision pedestrian tracking, effectively reducing ID switches and enhancing tracking robustness, timely detection of hazardous events in the engineering safety field to ensure personnel safety.
AbstractList	In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the issue of experiencing frequent switching of target identity ID switches (IDs). In response to the issues above, this paper proposes a multi-object tracking model that integrates improved You Only Look Once Version 8 (YOLOv8) and High-Performance Multi-Object Tracking by Tracking Bytes (ByteTrack). The model architecture is based on the paradigm of tracking-by-detection. In the detection stage, we combine the coordinate attention mechanism to propose the Coordinate Attention Spatial Pyramid Pooling - Fast Conv (CASPPFC) module, and combine it with improved Efficient Vision Transformer (EfficientViT) to enhance the YOLOv8 backbone network, effectively reducing false positives and false negatives caused by occlusion. In the first stage of tracking association, we propose the Omni-Scale Network-Coordinate Attention (OSNet-CA) network as the Re-identification (Re-ID) feature extraction method to capture target information more effectively. In the second stage of association, we adopt the Efficient Intersection over Union (EIoU) improvement method to comprehensively consider the positional relationships between targets. The effectiveness of the improved model is validated on the Multi-Object Tracking 2017 (MOT17) and Multi-Object Tracking 2020 (MOT20) datasets. The results indicate that our tracking model achieved 80.5% Multiple Object Tracking Accuracy (MOTA), 79.3% Identification F1 Score (IDF1), and 64.2% Higher Order Tracking Accuracy (HOTA) on the MOT17 test set, and 77.8% MOTA, 76.9% IDF1, and 62.4% HOTA on the MOT20 test set. This tracking model can achieve high-precision pedestrian tracking, effectively reducing ID switches and enhancing tracking robustness, timely detection of hazardous events in the engineering safety field to ensure personnel safety. In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the issue of experiencing frequent switching of target identity ID switches (IDs). In response to the issues above, this paper proposes a multi-object tracking model that integrates improved You Only Look Once Version 8 (YOLOv8) and High-Performance Multi-Object Tracking by Tracking Bytes (ByteTrack). The model architecture is based on the paradigm of tracking-by-detection. In the detection stage, we combine the coordinate attention mechanism to propose the Coordinate Attention Spatial Pyramid Pooling - Fast Conv (CASPPFC) module, and combine it with improved Efficient Vision Transformer (EfficientViT) to enhance the YOLOv8 backbone network, effectively reducing false positives and false negatives caused by occlusion. In the first stage of tracking association, we propose the Omni-Scale Network-Coordinate Attention (OSNet-CA) network as the Re-identification (Re-ID) feature extraction method to capture target information more effectively. In the second stage of association, we adopt the Efficient Intersection over Union (EIoU) improvement method to comprehensively consider the positional relationships between targets. The effectiveness of the improved model is validated on the Multi-Object Tracking 2017 (MOT17) and Multi-Object Tracking 2020 (MOT20) datasets. The results indicate that our tracking model achieved 80.5% Multiple Object Tracking Accuracy (MOTA), 79.3% Identification F1 Score (IDF1), and 64.2% Higher Order Tracking Accuracy (HOTA) on the MOT17 test set, and 77.8% MOTA, 76.9% IDF1, and 62.4% HOTA on the MOT20 test set. This tracking model can achieve high-precision pedestrian tracking, effectively reducing ID switches and enhancing tracking robustness, timely detection of hazardous events in the engineering safety field to ensure personnel safety.In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the issue of experiencing frequent switching of target identity ID switches (IDs). In response to the issues above, this paper proposes a multi-object tracking model that integrates improved You Only Look Once Version 8 (YOLOv8) and High-Performance Multi-Object Tracking by Tracking Bytes (ByteTrack). The model architecture is based on the paradigm of tracking-by-detection. In the detection stage, we combine the coordinate attention mechanism to propose the Coordinate Attention Spatial Pyramid Pooling - Fast Conv (CASPPFC) module, and combine it with improved Efficient Vision Transformer (EfficientViT) to enhance the YOLOv8 backbone network, effectively reducing false positives and false negatives caused by occlusion. In the first stage of tracking association, we propose the Omni-Scale Network-Coordinate Attention (OSNet-CA) network as the Re-identification (Re-ID) feature extraction method to capture target information more effectively. In the second stage of association, we adopt the Efficient Intersection over Union (EIoU) improvement method to comprehensively consider the positional relationships between targets. The effectiveness of the improved model is validated on the Multi-Object Tracking 2017 (MOT17) and Multi-Object Tracking 2020 (MOT20) datasets. The results indicate that our tracking model achieved 80.5% Multiple Object Tracking Accuracy (MOTA), 79.3% Identification F1 Score (IDF1), and 64.2% Higher Order Tracking Accuracy (HOTA) on the MOT17 test set, and 77.8% MOTA, 76.9% IDF1, and 62.4% HOTA on the MOT20 test set. This tracking model can achieve high-precision pedestrian tracking, effectively reducing ID switches and enhancing tracking robustness, timely detection of hazardous events in the engineering safety field to ensure personnel safety. Abstract In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the issue of experiencing frequent switching of target identity ID switches (IDs). In response to the issues above, this paper proposes a multi-object tracking model that integrates improved You Only Look Once Version 8 (YOLOv8) and High-Performance Multi-Object Tracking by Tracking Bytes (ByteTrack). The model architecture is based on the paradigm of tracking-by-detection. In the detection stage, we combine the coordinate attention mechanism to propose the Coordinate Attention Spatial Pyramid Pooling - Fast Conv (CASPPFC) module, and combine it with improved Efficient Vision Transformer (EfficientViT) to enhance the YOLOv8 backbone network, effectively reducing false positives and false negatives caused by occlusion. In the first stage of tracking association, we propose the Omni-Scale Network-Coordinate Attention (OSNet-CA) network as the Re-identification (Re-ID) feature extraction method to capture target information more effectively. In the second stage of association, we adopt the Efficient Intersection over Union (EIoU) improvement method to comprehensively consider the positional relationships between targets. The effectiveness of the improved model is validated on the Multi-Object Tracking 2017 (MOT17) and Multi-Object Tracking 2020 (MOT20) datasets. The results indicate that our tracking model achieved 80.5% Multiple Object Tracking Accuracy (MOTA), 79.3% Identification F1 Score (IDF1), and 64.2% Higher Order Tracking Accuracy (HOTA) on the MOT17 test set, and 77.8% MOTA, 76.9% IDF1, and 62.4% HOTA on the MOT20 test set. This tracking model can achieve high-precision pedestrian tracking, effectively reducing ID switches and enhancing tracking robustness, timely detection of hazardous events in the engineering safety field to ensure personnel safety.
ArticleNumber	31414
Author	Shi, Hong Hou, Mingxing Wu, Yiming Mu, Xiaofang
Author_xml	– sequence: 1 givenname: Mingxing surname: Hou fullname: Hou, Mingxing organization: School of Computer Science and Technology, Taiyuan Normal University – sequence: 2 givenname: Yiming surname: Wu fullname: Wu, Yiming organization: School of Computer Science and Technology, Taiyuan Normal University – sequence: 3 givenname: Hong surname: Shi fullname: Shi, Hong organization: School of Computer Science and Technology, Taiyuan Normal University – sequence: 4 givenname: Xiaofang surname: Mu fullname: Mu, Xiaofang email: mu_xiao_fang@163.com organization: Shanxi Institute of Energy
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40858785$$D View this record in MEDLINE/PubMed
BookMark	eNp9kk1vFSEUhompsfXaP-DCTOLGzSifM7AyTeNHkyZu6powzGEu1xmowLTx38vtrbV1IQsgh-e8HDjvS3QUYgCEXhP8nmAmP2ROhJItpqIlHZOq5c_QCcVctJRRevRof4xOc97hOgRVnKgX6JhjKWQvxQm6OmvKbWxyMRM0yzoX38RhB7Y0JRn7w4epMfMUky_bpbmt8z4esotpgdSYMDamFAjFx9AsYLcm-Ly8Qs-dmTOc3q8b9P3zp6vzr-3lty8X52eXreWKl1aoTg18NLhn3BkYDB8FiM510nYKCHGGjN2ADR2kYICZsiMZOsUrKTlXA9ugi4PuGM1OXye_mPRLR-P1XSCmSZtUvJ1BM9UTwJgOoBTvuJIcu1EZ56hzXAhXtT4etK7XYYHR1jclMz8RfXoS_FZP8UYTyiQhvagK7-4VUvy5Qi568dnCPJsAcc2aUV4bxaTYo2__QXdxTaH-1Z5iEtO-NnmD3jwu6aGWP92rAD0ANsWcE7gHhGC9d4k-uERXl-g7l2hek9ghKVc4TJD-3v2frN_ePb-E
Cites_doi	10.1038/s41598-024-58800-6 10.1109/CVPR.2018.00129 10.1109/CVPR.2018.00248 10.1016/j.neucom.2022.07.042 10.1109/CVPR52729.2023.00934 10.3390/s24196495 10.1109/CVPR.2018.00562 10.1007/s11263-021-01513-4 10.1109/CVPR.2018.00159 10.1109/ICIP.2016.7533003 10.1007/978-3-319-10602-1_48 10.1177/03611981241258753 10.1007/s11263-020-01375-2 10.1109/CVPR52729.2023.00721 10.1007/978-3-030-01264-9_48 10.1109/CVPR.2008.4587583 10.1109/CVPR46437.2021.01350 10.1007/978-3-319-48881-3_2 10.1115/1.3662552 10.1609/aaai.v34i07.6999 10.1109/WACVW54805.2022.00019 10.1109/ICCV.2015.133 10.1609/aaai.v38i7.28493 10.1109/ICCV.2019.00380 10.1007/978-3-030-01234-2_1 10.1109/TPAMI.2015.2389824 10.1007/978-3-030-58548-8_28 10.1109/CVPR.2019.00075 10.1109/ICIP.2017.8296962 10.1016/j.displa.2024.102814 10.1109/WACV56688.2023.00485 10.1002/nav.3800020109 10.1007/978-3-031-20047-2_1 10.1109/ICCV48922.2021.00295 10.1016/j.knosys.2025.113539 10.1109/CVPR.2018.00745 10.1109/TMM.2023.3240881 10.1109/CVPR.2018.00720
ContentType	Journal Article
Copyright	The Author(s) 2025 2025. The Author(s). The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025
Copyright_xml	– notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025
DBID	C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA
DOI	10.1038/s41598-025-16389-4
DatabaseName	Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic ProQuest - Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	Publicly Available Content Database MEDLINE - Academic PubMed
Database_xml	– sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database (subscription) url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	2045-2322
EndPage	14
ExternalDocumentID	oai_doaj_org_article_3971e002be994649840fd9aff2ff455f PMC12381175 40858785 10_1038_s41598_025_16389_4
Genre	Journal Article
GrantInformation_xml	– fundername: Technology Innovation Program for Higher Education Institutions in Shanxi Province grantid: 2022L403 – fundername: the Shanxi Province Basic Research Program grantid: 202303021211187
GroupedDBID	0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RNT RNTTT RPM SNYQT UKHRP AAYXX AFFHD CITATION NPM 3V. 7XB 88A 8FK K9. M48 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM
ID	FETCH-LOGICAL-c494t-5969b4da0734faeba4d5e56f68c69e11fa1d6b0a2b853e039cd1b694eba8449b3
IEDL.DBID	DOA
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001559641800006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2045-2322
IngestDate	Tue Oct 14 19:09:16 EDT 2025 Tue Nov 04 02:05:40 EST 2025 Sat Nov 01 14:08:15 EDT 2025 Sat Nov 01 15:08:06 EDT 2025 Thu Sep 04 05:00:40 EDT 2025 Sat Nov 29 07:35:24 EST 2025 Wed Aug 27 01:34:30 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Two-stage association Attention mechanism Multi-object tracking Re-identification You Only Look Once Version 8
Language	English
License	2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c494t-5969b4da0734faeba4d5e56f68c69e11fa1d6b0a2b853e039cd1b694eba8449b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://doaj.org/article/3971e002be994649840fd9aff2ff455f
PMID	40858785
PQID	3243802710
PQPubID	2041939
PageCount	14
ParticipantIDs	doaj_primary_oai_doaj_org_article_3971e002be994649840fd9aff2ff455f pubmedcentral_primary_oai_pubmedcentral_nih_gov_12381175 proquest_miscellaneous_3246383855 proquest_journals_3243802710 pubmed_primary_40858785 crossref_primary_10_1038_s41598_025_16389_4 springer_journals_10_1038_s41598_025_16389_4
PublicationCentury	2000
PublicationDate	2025-08-26
PublicationDateYYYYMMDD	2025-08-26
PublicationDate_xml	– month: 08 year: 2025 text: 2025-08-26 day: 26
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Scientific reports
PublicationTitleAbbrev	Sci Rep
PublicationTitleAlternate	Sci Rep
PublicationYear	2025
Publisher	Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio
References	X Dong (16389_CR16) 2024; 84 16389_CR52 Y Liu (16389_CR20) 2024; 14 16389_CR10 A Vaswani (16389_CR30) 2017; 30 16389_CR51 Y Du (16389_CR11) 2023; 25 J Luiten (16389_CR49) 2021; 129 K He (16389_CR32) 2015; 37 16389_CR46 16389_CR45 16389_CR48 16389_CR47 A Wang (16389_CR50) 2024; 37 Y Zhang (16389_CR2) 2021; 129 16389_CR22 16389_CR21 16389_CR19 16389_CR18 16389_CR12 16389_CR4 16389_CR3 X Dong (16389_CR17) 2025; 2679 16389_CR6 16389_CR5 16389_CR1 HW Kuhn (16389_CR14) 1955; 2 16389_CR31 16389_CR33 16389_CR8 16389_CR7 16389_CR9 16389_CR27 RE Kalman (16389_CR13) 1960; 82 16389_CR29 16389_CR24 16389_CR23 16389_CR26 16389_CR25 P Shi (16389_CR15) 2025; 318 16389_CR42 16389_CR41 16389_CR44 16389_CR43 16389_CR40 16389_CR39 16389_CR38 16389_CR35 Y-F Zhang (16389_CR28) 2022; 506 16389_CR34 16389_CR37 16389_CR36
References_xml	– volume: 14 start-page: 8768 year: 2024 ident: 16389_CR20 publication-title: Sci. Rep. doi: 10.1038/s41598-024-58800-6 – ident: 16389_CR40 doi: 10.1109/CVPR.2018.00129 – volume: 30 start-page: 526 year: 2017 ident: 16389_CR30 publication-title: Adv. Neural Inf. Process. Syst. – ident: 16389_CR38 doi: 10.1109/CVPR.2018.00248 – volume: 506 start-page: 146 year: 2022 ident: 16389_CR28 publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.07.042 – ident: 16389_CR9 doi: 10.1109/CVPR52729.2023.00934 – ident: 16389_CR24 doi: 10.3390/s24196495 – ident: 16389_CR39 doi: 10.1109/CVPR.2018.00562 – volume: 37 start-page: 107984 year: 2024 ident: 16389_CR50 publication-title: Adv. Neural. Inf. Process. Syst. – volume: 129 start-page: 3069 year: 2021 ident: 16389_CR2 publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-021-01513-4 – ident: 16389_CR36 doi: 10.1109/CVPR.2018.00159 – ident: 16389_CR18 – ident: 16389_CR43 – ident: 16389_CR7 doi: 10.1109/ICIP.2016.7533003 – ident: 16389_CR44 doi: 10.1007/978-3-319-10602-1_48 – ident: 16389_CR4 – volume: 2679 start-page: 1947 year: 2025 ident: 16389_CR17 publication-title: Transp. Res. Rec. doi: 10.1177/03611981241258753 – ident: 16389_CR34 – volume: 129 start-page: 548 year: 2021 ident: 16389_CR49 publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-020-01375-2 – ident: 16389_CR23 doi: 10.1109/CVPR52729.2023.00721 – ident: 16389_CR47 doi: 10.1007/978-3-030-01264-9_48 – ident: 16389_CR12 doi: 10.1109/CVPR.2008.4587583 – ident: 16389_CR25 doi: 10.1109/CVPR46437.2021.01350 – ident: 16389_CR48 doi: 10.1007/978-3-319-48881-3_2 – ident: 16389_CR51 – volume: 82 start-page: 35 year: 1960 ident: 16389_CR13 publication-title: J. Basic Eng. doi: 10.1115/1.3662552 – ident: 16389_CR42 doi: 10.1609/aaai.v34i07.6999 – ident: 16389_CR5 doi: 10.1109/WACVW54805.2022.00019 – ident: 16389_CR45 doi: 10.1109/ICCV.2015.133 – ident: 16389_CR29 – ident: 16389_CR52 doi: 10.1609/aaai.v38i7.28493 – ident: 16389_CR27 doi: 10.1109/ICCV.2019.00380 – ident: 16389_CR35 doi: 10.1007/978-3-030-01234-2_1 – ident: 16389_CR22 – volume: 37 start-page: 1904 year: 2015 ident: 16389_CR32 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2389824 – ident: 16389_CR1 doi: 10.1007/978-3-030-58548-8_28 – ident: 16389_CR41 doi: 10.1109/CVPR.2019.00075 – ident: 16389_CR31 – ident: 16389_CR8 doi: 10.1109/ICIP.2017.8296962 – volume: 84 start-page: 102814 year: 2024 ident: 16389_CR16 publication-title: Displays doi: 10.1016/j.displa.2024.102814 – ident: 16389_CR26 – ident: 16389_CR3 doi: 10.1109/WACV56688.2023.00485 – ident: 16389_CR46 – ident: 16389_CR21 – volume: 2 start-page: 83 year: 1955 ident: 16389_CR14 publication-title: Naval Res. Logist. Q. doi: 10.1002/nav.3800020109 – ident: 16389_CR10 doi: 10.1007/978-3-031-20047-2_1 – ident: 16389_CR19 – ident: 16389_CR6 doi: 10.1109/ICCV48922.2021.00295 – volume: 318 start-page: 113539 year: 2025 ident: 16389_CR15 publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2025.113539 – ident: 16389_CR33 doi: 10.1109/CVPR.2018.00745 – volume: 25 start-page: 8725 year: 2023 ident: 16389_CR11 publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2023.3240881 – ident: 16389_CR37 doi: 10.1109/CVPR.2018.00720
SSID	ssj0000529419
Score	2.457618
Snippet	In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well as the... Abstract In the field of engineering safety, multi-object tracking encounters difficulties in effectively conducting object detection due to occlusion, as well...
SourceID	doaj pubmedcentral proquest pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Publisher
StartPage	31414
SubjectTerms	639/166 639/705 639/705/117 Accuracy Algorithms Attention mechanism Boxes Deep learning Design Humanities and Social Sciences Localization Multi-object tracking multidisciplinary Neural networks Occlusion Re-identification Safety engineering Science Science (multidisciplinary) Two-stage association You Only Look Once Version 8
SummonAdditionalLinks	– databaseName: ProQuest - Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZgCxIX3tBAQUbiBtHmMXbtEyqICiSo9lBQOVl-bldik3aTgvj3jJ3sVsvrxNW2onHm4c8z4xlCnoMoPWi8lpggqxw0s6hzjOeO68JYHNSpJcvnD_tHR-LkRM7G59HdmFa5tonJUA_VnmPeNhrhqWtt9JhPEQbUAm9UZfHq7DyPPaRirHVsqHGV7MTCW8WE7Mzef5x92fhcYlQLSjm-nSlqMe3w_IpvzCqWR2Aic9g6n1IZ_z9hz99TKH-Jo6bj6fDW_93YbXJzhKn0YJCrO-SKb-6S60Pjyh_3yPEB7b-3FJHl3NOUk0hbEz06FMm00ftO9dc5frc_XdLo6o3jA0D2K6obR2NZz5RoSZc-Pj5edMv75NPh2-M37_KxP0NuQUKfM8mlAafRSkDQ3mhwzDMeuLBc-rIMunTcFLoyiAl8UUvrSsMl4EoBIE39gEyatvG7hBbOcbDCmUobCM4bGaCwhoPXCIhYnZEXa66os6EMh0rh81qogYcKeagSDxVk5HVk3GZlLKGdBtrVXI0aqRCIlR7PA-OlBA4Sb7rBSR1CFQIwFjKyt-aXGvW6U5fsycizzTRqZAyz6Ma3F2kNklELxjLycJCSDSWxnpzYFzgjtuRni9TtmWZxmqp-lxFcIdjLyMu1qF3S9fd_8ejf23hMblRR-gu0l3yPTPrVhX9Crtlv_aJbPR0V6CdCgCxJ priority: 102 providerName: ProQuest
Title	A two stage multi object tracking algorithm with transformer and attention mechanism
URI	https://link.springer.com/article/10.1038/s41598-025-16389-4 https://www.ncbi.nlm.nih.gov/pubmed/40858785 https://www.proquest.com/docview/3243802710 https://www.proquest.com/docview/3246383855 https://pubmed.ncbi.nlm.nih.gov/PMC12381175 https://doaj.org/article/3971e002be994649840fd9aff2ff455f
Volume	15
WOSCitedRecordID	wos001559641800006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database (subscription) customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagBYkL4k2grIzEDaLmMXbsY4tagURXESpoOVl2bLcrsUm1mxbx7xk72aXLQ1y4zMG2otGMJ_ONPZ4h5BWI3IHGsMR4WaSgWYM2x3hquc5Mg4M6tmT5_KGaTsVsJutrrb5CTthQHngQ3D76y9yh2RonJXCQGJB4K7X3hffAmA9_36yS14Kpoap3ISGX4yuZrBT7K_RU4TVZwdIAQWQKW54oFuz_E8r8PVnylxvT6IiO75G7I4KkBwPn98kN1z4gt4eekt8fktMD2n_rKIK-M0djuiDtTDhsofjdJhyMU_31rFvO-_MFDaewYXzArm5JdWtpqLgZcyDpwoV3wfPV4hH5dHx0-vZdOrZOSBuQ0KdMcmnAajRg8NoZDZY5xj0XDZcuz73OLTeZLgy6a5eVsrG54RJwpQCQpnxMdtqudU8Jzazl0AhrCm3AW2ekh6wxHJxGrMLKhLxei1FdDBUyVLzZLoUahK5Q6CoKXUFCDoOkNytDdes4gDpXo87Vv3SekL21ntRociuFyLAUGGTnWUJebqbRWMINiG5ddxnXIBulYCwhTwa1bjgJpd5EJXBGbCl8i9XtmXZ-Hgty5wH3IA5LyJv13vjJ199l8ex_yOI5uVOETZ3hD4_vkZ1-eelekFvNVT9fLSfkZjWrIhUTsnt4NK0_TqKlID0p6kArpLv1-5P6yw-nqRhX
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JbxMxFH4qBQQX9mVoASPBCUadxXbtQ4XKUrVqiHoIqDfXHttpJDJTkpSqf4rfyLMnkypstx642tbI9nx-77PfBvCSitxRjdcS42WRUs0qPHOMp5brzFTYqGNJli-9zX5fHB7KgxX40cXCBLfKTiZGQW2bKryRb6DiLwXeofLs7cm3NFSNCtbVroRGC4t9d36GV7bp1t4H_L-vimLn4-D9bjqvKpBWVNJZyiSXhlqN2KZeO6OpZY5xz0XFpctzr3PLTaYLg5rMZaWsbG64pDhSUCpNid-9AleRRhRZdBU8WLzpBKsZzeU8NicrxcYU9WOIYStYGoiPTOmS_otlAv7EbX930fzFThvV387t_23j7sCtOdEm2-3JuAsrrr4H19vSm-f3YbBNZmcNQW48dCR6VZLGhDcpghtRBfsB0V-HuI7Z8ZiEx-rQ3lJ8NyG6tiQkJo2uomTsQvj0aDp-AJ8vZUkPYbVuavcYSGYtp5WwptCGeuuM9DSrDKdOI6VjZQKvu_-uTtpEIio6AJRCtShRiBIVUaJoAu8CNBYjQxLw2NBMhmouUxRSydyhRjNOSsqpxLu6t1J7X3hPGfMJrHeIUHPJNFUXcEjgxaIbZUowFOnaNadxDE6jFIwl8KjF4WImISOe2BTYI5YQujTV5Z56dBzzlueBHiJdTeBNB-aLef19L578exnP4cbu4FNP9fb6-2twswhnLUPpz9dhdTY5dU_hWvV9NppOnsXDSuDoskH-E7MEfcE
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9QwFH4qZREX9iVQwEhwgmiyOB77gFChjKhajeZQUG_Gju3pSExSZlKq_jV-Hc9OMtWw3XrgaluR7Xx-77PfBvCC8tRShdcS7UQWU1WUeOYKFhumEl1iowolWT7vD8djfngoJhvwo4-F8W6VvUwMgtrUpX8jH6DizzneodJk4Dq3iMnO6O3xt9hXkPKW1r6cRguRPXt2ite35ZvdHfzXL7Ns9OHg_ce4qzAQl1TQJi4EE5oahTinTlmtqClswRzjJRM2TZ1KDdOJyjRqNZvkojSpZoLiSE6p0Dl-9xJcHvqsWN5tMJus3ne8BY2moovTSXI-WKKu9PFsWRF7EiRiuqYLQ8mAP_Hc3901f7HZBlU4uvk_b-ItuNERcLLdnpjbsGGrO3C1Lcl5dhcOtklzWhPkzFNLgrclqbV_qyK4KaW3KxD1dYrraI7mxD9i-_aW-tsFUZUhPmFpcCElc-vDqmfL-T34dCFLug-bVV3Zh0ASYxgtudGZ0tQZq4WjSakZtQqpXpFH8KrHgDxuE4zI4BiQc9kiRiJiZECMpBG88zBZjfTJwUNDvZjKTtZIpJipRU2nrRCUUYF3eGeEci5zjhaFi2CrR4fsJNZSnkMjguerbpQ13oCkKlufhDE4jZwXRQQPWkyuZuIz5fEhxx6-hta1qa73VLOjkM889bQRaWwEr3tgn8_r73vx6N_LeAbXENtyf3e89xiuZ_7YJagU2BZsNosT-wSulN-b2XLxNJxbAl8uGuM_AQdfhoU
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+two+stage+multi+object+tracking+algorithm+with+transformer+and+attention+mechanism&rft.jtitle=Scientific+reports&rft.au=Hou%2C+Mingxing&rft.au=Wu%2C+Yiming&rft.au=Shi%2C+Hong&rft.au=Mu%2C+Xiaofang&rft.date=2025-08-26&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2045-2322&rft.volume=15&rft_id=info:doi/10.1038%2Fs41598-025-16389-4&rft_id=info%3Apmid%2F40858785&rft.externalDocID=PMC12381175
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon