Related papers: TennisExpert: Towards Expert-Level Analytical Sports Video Understanding

TennisExpert: Towards Expert-Level Analytical Sports Video Understanding

URL: http://arxiv.org/abs/2603.13397v2
Date: Tue, 17 Mar 2026 16:02:52 GMT
Title: TennisExpert: Towards Expert-Level Analytical Sports Video Understanding
Abstract summary: Tennis is one of the most widely followed sports, generating extensive broadcast footage with strong potential for professional analysis, automated coaching, and real-time commentary.<n>However, automatic tennis understanding remains underexplored due to lack of large-scale benchmarks with fine-grained annotations and expert-level commentary.<n>To address these challenges, we introduce TennisVL, a large-scale tennis benchmark comprising over 200 professional matches (471.9 hours) and 40,000+ rally-level clips.<n>We propose TennisExpert, a multimodal tennis understanding framework that integrates a video semantic with a memory-augmented model built on Qwen3-VL-8B
Score: 16.625250626542208
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Tennis is one of the most widely followed sports, generating extensive broadcast footage with strong potential for professional analysis, automated coaching, and real-time commentary. However, automatic tennis understanding remains underexplored due to two key challenges: (1) the lack of large-scale benchmarks with fine-grained annotations and expert-level commentary, and (2) the difficulty of building accurate yet efficient multimodal systems suitable for real-time deployment. To address these challenges, we introduce TennisVL, a large-scale tennis benchmark comprising over 200 professional matches (471.9 hours) and 40,000+ rally-level clips. Unlike existing commentary datasets that focus on descriptive play-by-play narration, TennisVL emphasizes expert analytical commentary capturing tactical reasoning, player decisions, and match momentum. Furthermore, we propose TennisExpert, a multimodal tennis understanding framework that integrates a video semantic parser with a memory-augmented model built on Qwen3-VL-8B. The parser extracts key match elements (e.g., scores, shot sequences, ball bounces, and player locations), while hierarchical memory modules capture both short- and long-term temporal context. Experiments show that TennisExpert consistently outperforms strong proprietary baselines, including GPT-5, Gemini, and Claude, and demonstrates improved ability to capture tactical context and match dynamics. Our dataset and code are publicly available at https://github.com/LZYAndy/TennisExpert.

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