Updating a dataset of labelled objects on raw video sequences with unique object IDs()

We present an update to the previously published dataset known as SFU-HW-Objects-v1. The new dataset is called SFU-HW-Tracks-v1 and contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. For each video fr...

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Detalles Bibliográficos
Autores principales: Tanaka, Takehiro, Choi, Hyomin, Bajić, Ivan V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841997/
https://www.ncbi.nlm.nih.gov/pubmed/35198673
http://dx.doi.org/10.1016/j.dib.2022.107892
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author Tanaka, Takehiro
Choi, Hyomin
Bajić, Ivan V.
author_facet Tanaka, Takehiro
Choi, Hyomin
Bajić, Ivan V.
author_sort Tanaka, Takehiro
collection PubMed
description We present an update to the previously published dataset known as SFU-HW-Objects-v1. The new dataset is called SFU-HW-Tracks-v1 and contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking, which was not possible using SFU-HW-Objects-v1.
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spelling pubmed-88419972022-02-22 Updating a dataset of labelled objects on raw video sequences with unique object IDs() Tanaka, Takehiro Choi, Hyomin Bajić, Ivan V. Data Brief Data Article We present an update to the previously published dataset known as SFU-HW-Objects-v1. The new dataset is called SFU-HW-Tracks-v1 and contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking, which was not possible using SFU-HW-Objects-v1. Elsevier 2022-02-02 /pmc/articles/PMC8841997/ /pubmed/35198673 http://dx.doi.org/10.1016/j.dib.2022.107892 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Tanaka, Takehiro
Choi, Hyomin
Bajić, Ivan V.
Updating a dataset of labelled objects on raw video sequences with unique object IDs()
title Updating a dataset of labelled objects on raw video sequences with unique object IDs()
title_full Updating a dataset of labelled objects on raw video sequences with unique object IDs()
title_fullStr Updating a dataset of labelled objects on raw video sequences with unique object IDs()
title_full_unstemmed Updating a dataset of labelled objects on raw video sequences with unique object IDs()
title_short Updating a dataset of labelled objects on raw video sequences with unique object IDs()
title_sort updating a dataset of labelled objects on raw video sequences with unique object ids()
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841997/
https://www.ncbi.nlm.nih.gov/pubmed/35198673
http://dx.doi.org/10.1016/j.dib.2022.107892
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