I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Dataset)

Waldmann, Urs; Naik, Hemal; Nagy, Mate; Kano, Fumihiro; Couzin, Iain D.; Deussen, Oliver; Goldlücke, Bastian

Datensatz:
I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Dataset)

Datum der Erstveröffentlichung

2023

Autor:innen

Repositorium der Erstveröffentlichung

Zenodo

Version des Datensatzes

v1

DOI (Link zu den Daten)

https://doi.org/10.5281/zenodo.7989830

Link zur Lizenz

CC BY 4.0

Sammlungen

Informatik und Informationswissenschaft: Datensätze
Exzellenzcluster "Centre for the Advanced Study of Collective Behaviour": Datensätze

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Publikationsstatus

Published

Zusammenfassung

This data entry contains the annotated single pigeon data from the I-MuPPET GCPR 2022 paper.

This data contains our annotated single pigeon data with RGB images and seven distinct keypoint annotations. The experiments were carried out by Hemal Naik, Máté Nagy, Fumihiro Kano and Iain D. Couzin and were approved by the Regierungspräsidium Freiburg under the permit number Az. 35-9185.81/G-19/107.

Data was recorded by two Vicon Vue 2 cameras (1920x1080 pixels) at 50 Hz, four Vantage 5 and 26 Vero 2.2 sensors.

The dataset was originally created during the Ph.D. thesis work of Hemal Naik. The method to reproduce the setup and dataset can be found at https://mediatum.ub.tum.de/?id=1554403. An updated version of this dataset with 4K resolution (3D-POP) is available at https://doi.org/10.17617/3.HPBBC7. The code base is available at https://github.com/alexhang212/dataset-3dpop. The users can reproduce annotations or improve them by adding more key points.

Code for I-MuPPET available at https://github.com/urs-waldmann/i-muppet/.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

pose estimation, deep learning, pigeons

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Publikation

Beitrag zu einem Konferenzband

I-MuPPET : Interactive Multi-Pigeon Pose Estimation and Tracking

(2022) Waldmann, Urs; Naik, Hemal; Máté, Nagy; Kano, Fumihiro; Couzin, Iain D.; Deussen, Oliver; Goldlücke, Bastian

DOI: 10.1007/978-3-031-16788-1_31

Erschienen in: ANDRES, Björn, Hrsg. und andere. Pattern Recognition : 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings. Cham: Springer, 2022, S. 513-528. Lecture Notes in Computer Science. 13485. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-16787-4. Verfügbar unter: doi: 10.1007/978-3-031-16788-1_31

Zitieren

ISO 690

WALDMANN, Urs, Hemal NAIK, Mate NAGY, Fumihiro KANO, Iain D. COUZIN, Oliver DEUSSEN, Bastian GOLDLÜCKE, 2023. I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Dataset)

BibTex

RDF

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This data contains our annotated single pigeon data with RGB images and seven distinct keypoint annotations. The experiments were carried out by Hemal Naik, Máté Nagy, Fumihiro Kano and Iain D. Couzin and were approved by the Regierungspräsidium Freiburg under the permit number Az. 35-9185.81/G-19/107.

Data was recorded by two Vicon Vue 2 cameras (1920x1080 pixels) at 50 Hz, four Vantage 5 and 26 Vero 2.2 sensors.

The dataset was originally created during the Ph.D. thesis work of Hemal Naik. The method to reproduce the setup and dataset can be found at https://mediatum.ub.tum.de/?id=1554403. An updated version of this dataset with 4K resolution (3D-POP) is available at https://doi.org/10.17617/3.HPBBC7. The code base is available at https://github.com/alexhang212/dataset-3dpop. The users can reproduce annotations or improve them by adding more key points.

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Universitätsbibliographie

Ja

Datensatz:
I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking (Dataset)

Datum der Erstveröffentlichung

Autor:innen

Andere Beitragende

Repositorium der Erstveröffentlichung