arXiv:2605.23355v1 Announce Type: cross Abstract: Temporal Action Localization (TAL) has been extensively studied in generic video understanding, while fine-grained sports scenarios, such as professional badminton, remain underexplored due to their complex and subtle spatio-temporal dynamics. In this paper, we focus on fine-grained TAL in professional badminton videos and introduce a new benchmark dataset, Fine-Badminton, which consists of 31 matches with 29 fine-grained stroke categories, covering 2104 rallies and 27597 annotated actions. To effectively capture the intricate motion patterns i
The proliferation of advanced computer vision techniques and increasing computational power makes fine-grained temporal action localization in complex scenarios more feasible.
This development contributes to the broader sophistication of AI in understanding complex human actions, which has implications for various applications beyond sports analytics.
The availability of a new, fine-grained benchmark dataset for badminton action localization provides a specialized tool for training and evaluating AI models in this domain.
- · AI researchers (computer vision)
- · Sports analytics companies
- · Professional sports leagues
Improved AI models for fine-grained action analysis in sports and other complex activities.
Enhanced capabilities for automated coaching, refereeing, and personalized athlete training.
Potential for new forms of media consumption and interactive experiences in sports, driven by granular AI analysis.
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