# Towards Structured Analysis of Broadcast Badminton Videos

arXiv: https://arxiv.org/abs/1712.08714 · HTML: https://mlsanity.com/paper/1712.08714/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2017.

## Built on by (most cited first, 15 of 15)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis](https://mlsanity.com/paper/2306.04948/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches](https://mlsanity.com/paper/2109.06431/) (2021)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [VA-AR: Learning Velocity-Aware Action Representations with Mixture of Window Attention](https://mlsanity.com/paper/2503.11004/) (2025)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset](https://mlsanity.com/paper/2403.12385/) (2024)
- [BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports](https://mlsanity.com/paper/2502.21085/) (2025)
- [Automated Hit-frame Detection for Badminton Match Analysis](https://mlsanity.com/paper/2307.16000/) (2023)
- [Towards Real-Time Analysis of Broadcast Badminton Videos](https://mlsanity.com/paper/2308.12199/) (2023)
- [AI coach for badminton](https://mlsanity.com/paper/2403.08956/) (2024)
- [TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification](https://mlsanity.com/paper/2605.02558/) (2026)
- [Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization](https://mlsanity.com/paper/2605.23355/) (2026)
- [DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton](https://mlsanity.com/paper/2608.24439/) (2026)

## Abstract (arXiv)

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.