直接用视频识别击剑拳击的细微动作，准确率超90%
FACTS: Fine-Grained Action Classification for Tactical Sports
- 用Transformer直接处理原始视频，不用人体姿态估计
- 击剑动作识别准确率达90%，拳击达83.25%
- 开源8类击剑动作数据集，助力战术运动分析
在击剑、拳击等快节奏近身对抗性体育项目中，细微动作的细粒度分类面临运动复杂、速度快、动作微妙等挑战。传统依赖姿态估计或复杂传感器的方法难以精准捕捉动态。本文提出FACTS，一种基于Transformer的细粒度动作识别新方法，可直接处理原始视频，无需人体姿态估计和体表标记。该方法在击剑动作识别上达到90%准确率，在拳击动作上达到83.25%。此外，我们构建了一个公开可用的新数据集，包含8种详细击剑动作，填补了体育分析资源的空白。研究成果有助于提升训练质量、性能分析与观赛体验，为战术运动中的动作分类设立了新基准。
原文摘要 · Abstract (English)
Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges due to the complexity, speed, and nuance of movements. Traditional methods reliant on pose estimation or fancy sensor data often struggle to capture these dynamics accurately. We introduce FACTS, a novel transformer-based approach for fine-grained action recognition that processes raw video data directly, eliminating the need for pose estimation and the use of cumbersome body markers and sensors. FACTS achieves state-of-the-art performance, with 90% accuracy on fencing actions and 83.25% on boxing actions. Additionally, we present a new publicly available dataset featuring 8 detailed fencing actions, addressing critical gaps in sports analytics resources. Our findings enhance training, performance analysis, and spectator engagement, setting a new benchmark for action classification in tactical sports.
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