构建大规模篮球技能视频数据集，助力精细技能评估研究
BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation
- 收集全球3万余名球员4477小时视频，覆盖20项精细篮球技能
- 现有模型表现远低于人类水平，凸显长时序细粒度识别挑战
- 适合动作分析、体育智能、个性化训练等方向研究者使用
我们提出BASKET，一个大规模篮球视频数据集，用于精细技能评估。该数据集包含4,477小时视频，涵盖来自全球的32,232名篮球运动员。相比已有技能评估数据集，BASKET在性别、年龄、技术水平、地理分布等方面具有前所未有的多样性。数据集包含20项精细篮球技能，要求模型基于8-10分钟的球员精彩片段视频，预测每项技能的水平（如优秀、良好、一般、尚可、较差）。实证分析显示，当前最先进视频模型在此任务上显著落后于人类基准。我们相信BASKET可推动具备长程、细粒度识别能力的新视频模型发展，并在公平球探、个性化球员培养等特定领域发挥作用。数据集与代码已开源：https://github.com/yulupan00/BASKET。
原文摘要 · Abstract (English)
We present BASKET, a large-scale basketball video dataset for fine-grained skill estimation. BASKET contains 4,477 hours of video capturing 32,232 basketball players from all over the world. Compared to prior skill estimation datasets, our dataset includes a massive number of skilled participants with unprecedented diversity in terms of gender, age, skill level, geographical location, etc. BASKET includes 20 fine-grained basketball skills, challenging modern video recognition models to capture the intricate nuances of player skill through in-depth video analysis. Given a long highlight video (8-10 minutes) of a particular player, the model needs to predict the skill level (e.g., excellent, good, average, fair, poor) for each of the 20 basketball skills. Our empirical analysis reveals that the current state-of-the-art video models struggle with this task, significantly lagging behind the human baseline. We believe that BASKET could be a useful resource for developing new video models with advanced long-range, fine-grained recognition capabilities. In addition, we hope that our dataset will be useful for domain-specific applications such as fair basketball scouting, personalized player development, and many others. Dataset and code are available at https://github.com/yulupan00/BASKET.
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