Multi-camera multi-object tracking: A review of current trends and future advances

The nascent applicability of multi-camera tracking (MCT) in numerous real-world applications makes it a significant computer vision problem. While visual tracking of objects, especially in video obtained from single camera setup, has drawn huge research attention, the constant identification and tra...

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Main Authors: Amosa, T.I., Sebastian, P., Izhar, L.I., Ibrahim, O., Ayinla, L.S., Bahashwan, A.A., Bala, A., Samaila, Y.A.
Format: Article
Published: Elsevier B.V. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37331/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165946984&doi=10.1016%2fj.neucom.2023.126558&partnerID=40&md5=8624a36a76d31cb84fb895a70e700546
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spelling oai:scholars.utp.edu.my:373312023-10-04T08:41:39Z http://scholars.utp.edu.my/id/eprint/37331/ Multi-camera multi-object tracking: A review of current trends and future advances Amosa, T.I. Sebastian, P. Izhar, L.I. Ibrahim, O. Ayinla, L.S. Bahashwan, A.A. Bala, A. Samaila, Y.A. The nascent applicability of multi-camera tracking (MCT) in numerous real-world applications makes it a significant computer vision problem. While visual tracking of objects, especially in video obtained from single camera setup, has drawn huge research attention, the constant identification and tracking of targets as they transit across multiple cameras remains an open research problem. In addition to the linking of target appearance and trajectory information across frames, effective association of such data across multiple cameras is also very critical in MCT. Occlusion, appearance variability, camera motion, as well as nonrigid object structure and motion are widely recognized constraints and major sources of concerns in MCT. In recent years, several literatures have been contributed suggesting a variety of approaches to addressing various problems in MCT. However, studies that critically review and report the advances and trends of research in MCT are still limited. This current study presents a comprehensive and up-to-date review of visual object tracking in multi-camera settings. In this paper, we analyze and categorize existing works based on six crucial facets: problem formulation, adopted problem solving approach, data association requirements, mutual exclusion constraints, benchmark datasets, and performance metrics. Furthermore, the study summarizes the outcomes of 30 state-of-the-art MCT algorithms on common datasets to allow quantitative comparison and analysis of their experimental results. Finally, we examine recent advances in MCT and suggest some promising future research directions. © 2023 The Author(s) Elsevier B.V. 2023 Article NonPeerReviewed Amosa, T.I. and Sebastian, P. and Izhar, L.I. and Ibrahim, O. and Ayinla, L.S. and Bahashwan, A.A. and Bala, A. and Samaila, Y.A. (2023) Multi-camera multi-object tracking: A review of current trends and future advances. Neurocomputing, 552. ISSN 09252312 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165946984&doi=10.1016%2fj.neucom.2023.126558&partnerID=40&md5=8624a36a76d31cb84fb895a70e700546 10.1016/j.neucom.2023.126558 10.1016/j.neucom.2023.126558 10.1016/j.neucom.2023.126558
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The nascent applicability of multi-camera tracking (MCT) in numerous real-world applications makes it a significant computer vision problem. While visual tracking of objects, especially in video obtained from single camera setup, has drawn huge research attention, the constant identification and tracking of targets as they transit across multiple cameras remains an open research problem. In addition to the linking of target appearance and trajectory information across frames, effective association of such data across multiple cameras is also very critical in MCT. Occlusion, appearance variability, camera motion, as well as nonrigid object structure and motion are widely recognized constraints and major sources of concerns in MCT. In recent years, several literatures have been contributed suggesting a variety of approaches to addressing various problems in MCT. However, studies that critically review and report the advances and trends of research in MCT are still limited. This current study presents a comprehensive and up-to-date review of visual object tracking in multi-camera settings. In this paper, we analyze and categorize existing works based on six crucial facets: problem formulation, adopted problem solving approach, data association requirements, mutual exclusion constraints, benchmark datasets, and performance metrics. Furthermore, the study summarizes the outcomes of 30 state-of-the-art MCT algorithms on common datasets to allow quantitative comparison and analysis of their experimental results. Finally, we examine recent advances in MCT and suggest some promising future research directions. © 2023 The Author(s)
format Article
author Amosa, T.I.
Sebastian, P.
Izhar, L.I.
Ibrahim, O.
Ayinla, L.S.
Bahashwan, A.A.
Bala, A.
Samaila, Y.A.
spellingShingle Amosa, T.I.
Sebastian, P.
Izhar, L.I.
Ibrahim, O.
Ayinla, L.S.
Bahashwan, A.A.
Bala, A.
Samaila, Y.A.
Multi-camera multi-object tracking: A review of current trends and future advances
author_facet Amosa, T.I.
Sebastian, P.
Izhar, L.I.
Ibrahim, O.
Ayinla, L.S.
Bahashwan, A.A.
Bala, A.
Samaila, Y.A.
author_sort Amosa, T.I.
title Multi-camera multi-object tracking: A review of current trends and future advances
title_short Multi-camera multi-object tracking: A review of current trends and future advances
title_full Multi-camera multi-object tracking: A review of current trends and future advances
title_fullStr Multi-camera multi-object tracking: A review of current trends and future advances
title_full_unstemmed Multi-camera multi-object tracking: A review of current trends and future advances
title_sort multi-camera multi-object tracking: a review of current trends and future advances
publisher Elsevier B.V.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37331/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165946984&doi=10.1016%2fj.neucom.2023.126558&partnerID=40&md5=8624a36a76d31cb84fb895a70e700546
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score 13.222552