SportsShot: A Fine-Grained Dataset for Shot Segmentation in Multiple Sports

Organized by sportsshot - Current server time: Jan. 10, 2025, 1:18 a.m. UTC

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Nov. 25, 2024, midnight UTC

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Motivation

Shot segmentation is an important and challenging task in video understanding, aiming to split videos into classified shots (e.g., close-up, close shot, full view, audience, transition, zooming, and others).

The existing datasets and benchmarks either simply focus on shot boundary detection (e.g., ClipShots and SHOT) or lack well-defined shot categories for segmentation (e.g., SoccerNet-v2). As a result, it is necessary to propose a new dataset with fine-grained shot categories for shot segementation and well-defined shot boundaries for shot boundary detection.

To this purpose, we propose a fine-grained dataset for shot segmentation as well as shot boundary detection in multiple sports scenes, coined as SportsShot. Our SportsShot is characterized with important properties of well-defined shot boundaries, fine-grained shot categories of complexity, and high-quality annotations with consistency, resulting in more challenges in both shot segmentation and boundary detection. For shot segmentation, we define seven semantic categories with complexity and close to human understanding. As for shot boundary detection, we view both hard cuts and gradual transitions as boundaries and annotate them as intervals.

Organizers and Emails

This track is provide by MCG Group @ Nanjing University

Evaluation

We use accuracy and segmental F1 scores to analyze shot segmentation performance following the standard practice in the temporal action segmentation task[1], in which accuracy evaluates the predictions in a frame-wise manner, while segmental F1-scores measure the temporal overlap between predicted and ground truth segments at different thresholds.

For shot boundary detection, we utilize the precision, recall, and F1 scores following previous datasets[2, 3], for it is important to detect shot boundaries both precisely and thoroughly. To take the duration of gradual transitions into consideration, we label a detected shot boundary as a positive result only when its temporal IoU with a gt shot boundary is over 0.5.

Reference

  • [1]: Fangqiu Yi, Hongyu Wen, and Tingting Jiang. ASFormer: Transformer for Action Segmentation. In BritishMachine Vision Conference (BMVC), 2021.
  • [2]: Shitao Tang, Litong Feng, Zhanghui Kuang, Yimin Chen, and Wei Zhang. Fast video shot transition localization with deep structured models. In Asian Conference on Computer Vision, pages 577–592. Springer, 2018.
  • [3]: Wentao Zhu, Yufang Huang, Xiufeng Xie, Wenxian Liu, Jincan Deng, Debing Zhang, ZhangyangWang, and Ji Liu. Autoshot: A short video dataset and state-of-the-art shot boundary detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2237–2246, 2023.

Terms and Conditions

  • You agree to us storing your submission results for evaluation purposes.
  • You agree not to distribute SportsShot dataset without prior written permission.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Data Format

Please ensure that the submitted data contains results of all of the test videos.

Expected submission data:

  • ZIP_THIS_FOLDER
    • seg
      • v_0mcffpH2VTw_0.txt
      • v_0mcffpH2VTw_1.txt
      • ......(all of test videos)
    • det
      • v_0mcffpH2VTw_0.txt
      • v_0mcffpH2VTw_1.txt
      • ......(all of test videos)

The submission data for shot segmentation should contain one label per line for each frame:

close_up
...
close_up
full_view
...
full_view
...

The submission data for shot boundary detection should contain both start frame and end frame per line:

21 25
78 79
122 123
...

Test

Start: Nov. 25, 2024, midnight

Description: To submit, upload a .zip file containing text files with the prediction.

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