Data Publication

Deep Video Understanding Annotations Dataset

Keith Curtis, George Awad, Ian Soboroff, Shahzad Rajput

Contact: George Awad.

Identifier: doi:10.18434/mds2-2535

Described in this article:

https://dl.acm.org/doi/abs/10.1145/3372278.3390742https://dl.acm.org/doi/10.1145/3394171.3416303https://dl.acm.org/doi/10.1145/3394171.3416292https://dl.acm.org/doi/10.1145/3394171.3416305

Version: 1.0 First Released: 2022-01-25 Revised: 2022-01-25

The BBC Land Girls TV series is a 3 season series. Each season is 5 episodes of about 45mins each. The TRECVID group at NIST worked with the BBC Corp. to release the dataset to the research community to work on video understanding tasks. Unfortunately, the hosting arrangement for the dataset was not successful and the release of the video dataset couldn't be done. We are releasing the annotations conducted by NIST, without any video data, so that the researchers interested in working on knowledge graph understanding and natural language analysis can take advantage of them.

Research Areas

NIST R&D: Information Technology: Data and informaticsMathematics and Statistics: Image and signal processingInformation Technology: Software research

Keywords: Video AnalysisMultimedia UnderstandingBBCLand GirlsKnowledge GraphsShow more...

These data are public.

Data and related material can be found at the following locations:

Land Girls Annotations Home Page

Please read this guide to understand the components of this dataset, how it is formatted and possible ways to use it in your own research.

https://dl.acm.org/doi/abs/10.1145/3372278.3390742https://dl.acm.org/doi/10.1145/3394171.3416303https://dl.acm.org/doi/10.1145/3394171.3416292https://dl.acm.org/doi/10.1145/3394171.3416305

Version: 1.0 First Released: 2022-01-25 Revised: 2022-01-25

Cite this dataset

Keith Curtis, George Awad, Ian Soboroff, Shahzad Rajput (2021), Deep Video Understanding Annotations Dataset, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2535 (Accessed 2025-04-24)

Repository Metadata

Machine-readable descriptions of this dataset are available in the following formats:

NERDm

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