RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis

• URL: http://arxiv.org/abs/2511.17045v2
• Date: Thu, 27 Nov 2025 05:13:43 GMT
• Title: RacketVision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis
• Authors: Linfeng Dong, Yuchen Yang, Hao Wu, Wei Wang, Yuenan Hou, Zhihang Zhong, Xiao Sun,
• Abstract summary: RacketVision is a novel dataset for advancing computer vision in sports analytics.<n>It provides large-scale, fine-grained annotations for racket pose alongside traditional ball positions.<n>It is designed to tackle three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation, and predictive ball trajectory forecasting.
• Score: 27.850804936572896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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