{"id":648668,"date":"2025-01-10T20:31:01","date_gmt":"2025-01-10T17:31:01","guid":{"rendered":""},"modified":"2025-01-10T20:31:01","modified_gmt":"2025-01-10T17:31:01","slug":"a-dataset-with-detailed-annotation-and-rich-semantics","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/a-dataset-with-detailed-annotation-and-rich-semantics\/","title":{"rendered":"#A dataset with detailed annotation and rich semantics"},"content":{"rendered":"
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\n Credit: Pixabay\/CC0 Public Domain
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Group activity recognition (GAR), which aims to identify activities performed collectively in videos, has gained significant attention recently. Existing GAR datasets typically annotate only a single Group Activity (GA) instance per sample, carefully selected from original videos.<\/p>\n

This app<\/a>roach, while precise, diverges significantly from real-world contexts, which often involve multiple GA instances. Moreover, single word-level annotations are insufficient to encapsulate the complex semantic information in GA, thereby constraining the expansion and research of other GA-related tasks.<\/p>\n

To mitigate these limitations, a research team led by Wang Yun-Hong (Beihang University, China) published their research<\/a> on 15 December 2024 in Frontiers of Computer Science<\/a><\/i>.<\/p>\n

The team proposed FIFAWC, a novel dataset for GAR characterized by three notable distinctions:<\/p>\n

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  1. Comprehensive annotation: They thoroughly annotate all included GAs in each sample and retain the original frame count, diverging from previous datasets that focus on a single GA annotation and uniform frame normalization, which enhances the dataset’s complexity and practical application potential for advanced research.<\/li>\n
  2. Semantic description: Each clip in FIFAWC is accompanied by an elaborate caption from sports commentators, ensuring content accuracy and professionalism. This positions FIFAWC as a data foundation for a variety of tasks, such as video captioning and retrieval.<\/li>\n
  3. New scenario: FIFAWC marks a novel divergence from previous ones by featuring soccer match footage. The expansive spatial areas and rapid movements characteristic of soccer introduce new challenges, such as dynamic camera movements and smaller targets in frames, significantly elevating the complexity and difficulty of GAR.<\/li>\n<\/ol>\n

    In the research, they benchmark FIFAWC on two tasks: traditional GAR and innovative GA video captioning. For GAR, they evaluate the classical detector-based approach ARG, and the state-of-the-art detector-free DFWSGAR.<\/p>\n

    The results reveal high accuracy at category level, but low accuracy at sample level because of multiple GAs per sample, reflecting the complexity and challenge of FIFAWC. Compared to the exemplary performance (25.87 in terms of CIDEr) of PDVC on the ActivityNet dataset, the poor performance on FIFAWC indicates that further research is necessary for GA video captioning.<\/p>\n

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    More information:<\/strong>
    \n\t\t\t\t\t\t\t\t\t\t\t\tDuoxuan Pei et al, FIFAWC: a dataset with detailed annotation and rich semantics for group activity recognition, Frontiers of Computer Science<\/i> (2024).     DOI: 10.1007\/s11704-024-40027-3<\/a><\/p>\n<\/div>\n

    \n Provided by
    \n Higher Education Press<\/p>\n

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    \n Citation<\/strong>:
    \n Group activity recognition: A dataset with detailed annotation and rich semantics (2025, January 10)
    \n retrieved 10 January 2025
    \n from https:\/\/techxplore.com\/    news<\/a>\/2025-01-group-recognition-dataset-annotation-rich.html\n <\/p>\n

    \n This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
    \n part may be reproduced without the written permission. The content is provided for information purposes only.\n <\/p>\n<\/p><\/div>\n<\/p><\/div>\n