用姿态识别支持规则驱动的多媒体决策，以击剑判罚为例。
FERA: A Pose-Based Framework for Rule-Grounded Multimedia Decision Support with a Foil Fencing Case Study
- 基于姿态追踪与运动建模，将动作转化为可验算的规则输入。
- 在击剑判罚任务中达0.624准确率与0.632宏平均F1。
- 适合需要可解释性、可审计性的实时决策场景。
多媒体决策支持不仅需要识别，更需能被规则检验、人工审计并供下游逻辑使用的显式状态估计。本文提出击剑裁判助手（FERA），一种基于姿态的规则驱动决策框架，并以击剑中的双人快速移动与优先权规则为案例研究。该框架包含：标准参与者追踪、运动特征编码、校准的时间感知、紧凑的结构化决策层及面向解释的检索界面。我们还发布了经审核的基准数据集，包含标注明确且固定划分的训练/测试集，支持可复现评估。在统一协议下，轻量级深度旁路模块提升了最优图模型表现；基于二维特征流的紧凑结构分类器在最终左/右/无判定任务中达到0.624准确率与0.632宏平均F1分数。案例研究揭示了通用设计原则：明确感知与规则应用的边界，保留不确定性，并根据下游需求选择感知前端。
原文摘要 · Abstract (English)
Multimedia decision support requires more than recognition; it requires explicit state estimates that can be checked against rules, audited by humans, and consumed by downstream decision logic. We present the FEncing Referee Assistant (FERA), a pose-based framework for this setting, and study it through foil fencing, where decisions depend on fast bilateral motion and right-of-way rules. The framework separates canonical participant tracking, kinematic tokenization, calibrated temporal perception, a compact structured decision layer, and an explanation-oriented retrieval interface. We also release an audited benchmark with adjudicated labels and fixed folds for reproducible evaluation. Under a shared protocol, a lightweight lifted-depth sidecar strengthens the best graph-based perception model, while a compact structured classifier on the fixed two-dimensional token stream reaches 0.624 accuracy and a 0.632 macro-averaged F1 score on the final Left / Right / None decision. The case study supports a broader design lesson: keep the boundary between perception and rule application explicit, preserve uncertainty, and choose the perception front end according to the downstream operating point.
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