CAS-FD：面向单视角犯规与假摔识别的接触感知时间采样

中文摘要 AI 辅助

在足球运动中，区分真实犯规与模拟假摔仍是最具争议的细粒度识别问题之一，尤其是当必须从单一转播视角而非多视角摄像机角度做出此类判定时。我们推出了包含600个片段的均衡单视角犯规/假摔数据集，并表明接触感知采样（将模型注意力集中在身体接触时刻而非平等对待所有帧）能显著提升这一接触特定问题的识别性能。所提方法在保留的测试分割上达到86.0%的准确率和宏F1值0.860，较无接触感知的替代方法提升12个百分点，且在未见过的数据上提升幅度进一步增大。我们还针对人工标注评估了每个流水线组件，明确了系统成功与失败的位置及原因。最终成果为一个已记录的数据集、一个可复现的单视角流水线，以及一个用于转播足球画面中细粒度接触事件识别的基于实证的评估框架。该数据集和代码可在https://URL tamim/contact-aware-dive获取。

英文摘要

Distinguishing a genuine foul from a simulated dive in football remains one of the sport's most contested fine-grained recognition problems, especially when such decisions have to be from a single broadcast view without multi-view camera angle. We introduce a balanced 600-clip single-view Foul/Dive dataset and show that contact-aware sampling concentrating the model's attention around the moment of physical contact rather than treating all frames equally yields substantially improved recognition of this contact- specific problem. The proposed approach achieves 86.0% accuracy and macro-F1 0.860 on the held-out test split, a 12 percentage- point gain over contact-unaware alternatives that grows further on unseen data. We also evaluate each pipeline component against human annotations, establishing where and why the system suc- ceeds and fails. The result is a documented dataset, a reproducible single-view pipeline, and a grounded evaluation framework for fine-grained contact-event recognition in broadcast football footage. The dataset and code are available at https://github.com/hossain- tamim/contact-aware-dive.

发表机构

• Premier University（普雷米尔大学）

机构由 AI 辅助整理，请以论文原文为准。