Updating a dataset of labelled objects on raw video sequences with unique object IDs.
| Autor: | Tanaka T; School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada., Choi H; School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada., Bajić IV; School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. |
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| Jazyk: | angličtina |
| Zdroj: | Data in brief [Data Brief] 2022 Feb 02; Vol. 41, pp. 107892. Date of Electronic Publication: 2022 Feb 02 (Print Publication: 2022). |
| DOI: | 10.1016/j.dib.2022.107892 |
| Abstrakt: | 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. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Author(s). Published by Elsevier Inc.) |
| Databáze: | MEDLINE |
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