构建首个真实拳击多标签动作数据集，助力精准分析拳手技战术表现。
BoxMAC -- A Boxing Dataset for Multi-label Action Classification
- 基于15名职业拳手拍摄超6万帧视频，标注13类动作
- 提出联合识别单帧与视频中多重动作的新架构
- 适合体育分析、动作识别研究者使用
在竞技格斗项目如拳击中，分析运动员的技战术统计数据对评估比赛中的出拳数量与种类至关重要，这些数据常用于教练指导与表现提升。我们提出BoxMAC，一个真实场景下的拳击数据集，包含15位职业拳手，涵盖13种不同动作标签。数据集共包含超过60,000帧，由拳击教练参与逐帧标注，每帧可包含多个动作标签。由于两名拳手可能在同一时间戳执行不同出拳，该任务属于多标签动作分类范畴。我们提出一种新型网络架构，用于同时识别图像和视频中的多重动作。我们还研究了基于深度神经网络的基准模型以应对两类任务。我们认为，BoxMAC将有助于研究人员和从业者开发并评估更高效的性能分析模型。凭借其真实性和多样性，该数据集可为拳击运动的技术进步提供重要支持。
原文摘要 · Abstract (English)
In competitive combat sports like boxing, analyzing a boxers's performance statics is crucial for evaluating the quantity and variety of punches delivered during bouts. These statistics provide valuable data and feedback, which are routinely used for coaching and performance enhancement. We introduce BoxMAC, a real-world boxing dataset featuring 15 professional boxers and encompassing 13 distinct action labels. Comprising over 60,000 frames, our dataset has been meticulously annotated for multiple actions per frame with inputs from a boxing coach. Since two boxers can execute different punches within a single timestamp, this problem falls under the domain of multi-label action classification. We propose a novel architecture for jointly recognizing multiple actions in both individual images and videos. We investigate baselines using deep neural network architectures to address both tasks. We believe that BoxMAC will enable researchers and practitioners to develop and evaluate more efficient models for performance analysis. With its realistic and diverse nature, BoxMAC can serve as a valuable resource for the advancement of boxing as a sport
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