RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910003531612160 |
|---|---|
| author | Dong, Linfeng Yang, Yuchen Wu, Hao Wang, Wei Hou, Yuenan Zhong, Zhihang Sun, Xiao |
| author_facet | Dong, Linfeng Yang, Yuchen Wu, Hao Wang, Wei Hou, Yuenan Zhong, Zhihang Sun, Xiao |
| contents | We introduce RacketVision, a novel dataset and benchmark for advancing computer vision in sports analytics, covering table tennis, tennis, and badminton. The dataset is the first to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. It is designed to tackle three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation, and predictive ball trajectory forecasting. Our evaluation of established baselines reveals a critical insight for multi-modal fusion: while naively concatenating racket pose features degrades performance, a CrossAttention mechanism is essential to unlock their value, leading to trajectory prediction results that surpass strong unimodal baselines. RacketVision provides a versatile resource and a strong starting point for future research in dynamic object tracking, conditional motion forecasting, and multimodal analysis in sports. Project page at https://github.com/OrcustD/RacketVision |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis Dong, Linfeng Yang, Yuchen Wu, Hao Wang, Wei Hou, Yuenan Zhong, Zhihang Sun, Xiao Computer Vision and Pattern Recognition Artificial Intelligence Multimedia We introduce RacketVision, a novel dataset and benchmark for advancing computer vision in sports analytics, covering table tennis, tennis, and badminton. The dataset is the first to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. It is designed to tackle three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation, and predictive ball trajectory forecasting. Our evaluation of established baselines reveals a critical insight for multi-modal fusion: while naively concatenating racket pose features degrades performance, a CrossAttention mechanism is essential to unlock their value, leading to trajectory prediction results that surpass strong unimodal baselines. RacketVision provides a versatile resource and a strong starting point for future research in dynamic object tracking, conditional motion forecasting, and multimodal analysis in sports. Project page at https://github.com/OrcustD/RacketVision |
| title | RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia |
| url | https://arxiv.org/abs/2511.17045 |