BrackishMOT: The Brackish Multi-Object Tracking Dataset

Malte Pedersen; Daniel Lehotský; Ivan Adriyanov Nikolov; Thomas B. Moeslund

doi:https://arxiv.org/pdf/2302.10645.pdf

BrackishMOT: The Brackish Multi-Object Tracking Dataset

Malte Pedersen, Daniel Lehotský, Ivan Adriyanov Nikolov, Thomas B. Moeslund

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

2 Citationer (Scopus)

Abstract

There exist no publicly available annotated underwater multi-object tracking (MOT) datasets captured in turbid environments. To remedy this we propose the BrackishMOT dataset with focus on tracking schools of small fish, which is a notoriously difficultMOT task. BrackishMOT consists of 98 sequences captured in the wild. Alongside the novel dataset, we present baseline results by training a state-of-the-art tracker. Additionally, we propose a framework for creating
synthetic sequences in order to expand the dataset. The framework consists of animated fish models and realistic underwater environments. We analyse the effects of including synthetic data during training and show that a combination of real and synthetic underwater training data can enhance tracking performance. Links to code and data can be found at https://www.vap.aau.dk/brackishmot.

Originalsprog	Engelsk
Titel	Image Analysis : 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I.
Redaktører	Rikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
Antal sider	17
Forlag	Springer
Publikationsdato	apr. 2023
Sider	17-33
ISBN (Trykt)	978-3-031-31434-6
ISBN (Elektronisk)	978-3-031-31435-3
DOI	https://doi.org/https://arxiv.org/pdf/2302.10645.pdf https://doi.org/10.1007/978-3-031-31435-3_2
Status	Udgivet - apr. 2023
Begivenhed	Scandinavian Conference on Image Analysis - Levi, Finland Varighed: 18 apr. 2023 → 21 apr. 2023

Konference

Konference	Scandinavian Conference on Image Analysis
Land/Område	Finland
By	Levi
Periode	18/04/2023 → 21/04/2023

Navn	Lecture Notes in Computer Science
Vol/bind	LNCS 13885
ISSN	0302-9743

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Link to publication in Scopus

Marine Analytics using Computer Vision
Pedersen, M.
01/01/2020 → 01/01/2023
Projekter: Projekt › Forskning

BrackishMOT
Pedersen, M. (Ophavsperson), Lehotský, D. (Ophavsperson), Nikolov, I. A. (Ophavsperson) & Moeslund, T. B. (Ophavsperson), Kaggle, 22 feb. 2023
DOI: 10.34740/kaggle/ds/2695511, https://www.kaggle.com/datasets/maltepedersen/brackishmot
Datasæt

Citationsformater

Pedersen, M., Lehotský, D., Nikolov, I. A., & Moeslund, T. B. (2023). BrackishMOT: The Brackish Multi-Object Tracking Dataset. I R. Gade, M. Felsberg, & J.-K. Kämäräinen (red.), Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I. (s. 17-33). Springer. https://doi.org/https://arxiv.org/pdf/2302.10645.pdf, https://doi.org/10.1007/978-3-031-31435-3_2

Pedersen, Malte ; Lehotský, Daniel ; Nikolov, Ivan Adriyanov et al. / BrackishMOT : The Brackish Multi-Object Tracking Dataset. Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I.. red. / Rikke Gade ; Michael Felsberg ; Joni-Kristian Kämäräinen. Springer, 2023. s. 17-33 (Lecture Notes in Computer Science, Bind LNCS 13885).

@inproceedings{a05663d2711e4c55be36de977a17bb6b,

title = "BrackishMOT: The Brackish Multi-Object Tracking Dataset",

abstract = "There exist no publicly available annotated underwater multi-object tracking (MOT) datasets captured in turbid environments. To remedy this we propose the BrackishMOT dataset with focus on tracking schools of small fish, which is a notoriously difficultMOT task. BrackishMOT consists of 98 sequences captured in the wild. Alongside the novel dataset, we present baseline results by training a state-of-the-art tracker. Additionally, we propose a framework for creatingsynthetic sequences in order to expand the dataset. The framework consists of animated fish models and realistic underwater environments. We analyse the effects of including synthetic data during training and show that a combination of real and synthetic underwater training data can enhance tracking performance. Links to code and data can be found at https://www.vap.aau.dk/brackishmot.",

keywords = "Dataset, Fish, Multi-Object Tracking, Synthetic data, Underwater",

author = "Malte Pedersen and Daniel Lehotsk{\'y} and Nikolov, {Ivan Adriyanov} and Moeslund, {Thomas B.}",

year = "2023",

month = apr,

doi = "https://arxiv.org/pdf/2302.10645.pdf",

language = "English",

isbn = "978-3-031-31434-6",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "17--33",

editor = "Rikke Gade and Michael Felsberg and Joni-Kristian K{\"a}m{\"a}r{\"a}inen",

booktitle = "Image Analysis",

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note = "Scandinavian Conference on Image Analysis ; Conference date: 18-04-2023 Through 21-04-2023",

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Pedersen, M , Lehotský, D , Nikolov, IA & Moeslund, TB 2023, BrackishMOT: The Brackish Multi-Object Tracking Dataset. i R Gade, M Felsberg & J-K Kämäräinen (red), Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I.. Springer, Lecture Notes in Computer Science, bind LNCS 13885, s. 17-33, Scandinavian Conference on Image Analysis, Levi, Finland, 18/04/2023. https://doi.org/https://arxiv.org/pdf/2302.10645.pdf, https://doi.org/10.1007/978-3-031-31435-3_2

BrackishMOT: The Brackish Multi-Object Tracking Dataset. / Pedersen, Malte ; Lehotský, Daniel ; Nikolov, Ivan Adriyanov et al.
Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I.. red. / Rikke Gade; Michael Felsberg; Joni-Kristian Kämäräinen. Springer, 2023. s. 17-33 (Lecture Notes in Computer Science, Bind LNCS 13885).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - BrackishMOT

T2 - Scandinavian Conference on Image Analysis

AU - Pedersen, Malte

AU - Lehotský, Daniel

AU - Nikolov, Ivan Adriyanov

AU - Moeslund, Thomas B.

PY - 2023/4

Y1 - 2023/4

N2 - There exist no publicly available annotated underwater multi-object tracking (MOT) datasets captured in turbid environments. To remedy this we propose the BrackishMOT dataset with focus on tracking schools of small fish, which is a notoriously difficultMOT task. BrackishMOT consists of 98 sequences captured in the wild. Alongside the novel dataset, we present baseline results by training a state-of-the-art tracker. Additionally, we propose a framework for creatingsynthetic sequences in order to expand the dataset. The framework consists of animated fish models and realistic underwater environments. We analyse the effects of including synthetic data during training and show that a combination of real and synthetic underwater training data can enhance tracking performance. Links to code and data can be found at https://www.vap.aau.dk/brackishmot.

AB - There exist no publicly available annotated underwater multi-object tracking (MOT) datasets captured in turbid environments. To remedy this we propose the BrackishMOT dataset with focus on tracking schools of small fish, which is a notoriously difficultMOT task. BrackishMOT consists of 98 sequences captured in the wild. Alongside the novel dataset, we present baseline results by training a state-of-the-art tracker. Additionally, we propose a framework for creatingsynthetic sequences in order to expand the dataset. The framework consists of animated fish models and realistic underwater environments. We analyse the effects of including synthetic data during training and show that a combination of real and synthetic underwater training data can enhance tracking performance. Links to code and data can be found at https://www.vap.aau.dk/brackishmot.

KW - Dataset

KW - Fish

KW - Multi-Object Tracking

KW - Synthetic data

KW - Underwater

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U2 - https://arxiv.org/pdf/2302.10645.pdf

DO - https://arxiv.org/pdf/2302.10645.pdf

M3 - Article in proceeding

SN - 978-3-031-31434-6

T3 - Lecture Notes in Computer Science

SP - 17

EP - 33

BT - Image Analysis

A2 - Gade, Rikke

A2 - Felsberg, Michael

A2 - Kämäräinen, Joni-Kristian

PB - Springer

Y2 - 18 April 2023 through 21 April 2023

ER -

Pedersen M , Lehotský D , Nikolov IA , Moeslund TB. BrackishMOT: The Brackish Multi-Object Tracking Dataset. I Gade R, Felsberg M, Kämäräinen JK, red., Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I.. Springer. 2023. s. 17-33. (Lecture Notes in Computer Science, Bind LNCS 13885). doi: https://arxiv.org/pdf/2302.10645.pdf, 10.1007/978-3-031-31435-3_2

BrackishMOT: The Brackish Multi-Object Tracking Dataset

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