基于3D姿态的集成框架用于板球击球分类与自动生物力学分析
A 3D Pose-Based Ensemble Framework for Cricket Shot Classification and Automated Biomechanical Analysis
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中文总结 AI 辅助
本文提出一个基于3D姿态的集成框架，利用YOLO和MeTRAbs提取击球手姿态，实现四种板球击球的分类（准确率97.68%）并提供生物力学反馈，用于辅助教练和预防受伤。
中文摘要 AI 辅助
板球是全球最受推崇的运动之一，技术进步已深深融入现代比赛分析和教练指导的方式中。板球击球分类和自动化性能分析为这一趋势增添了新的维度。传统方法依赖RGB视频特征或静态图像，这些方法对摄像机角度、光照和背景杂乱等环境变化敏感，且往往无法捕捉击球动作的潜在生物力学特征。在本文中，我们提出一个改进板球教练指导的系统，该系统接收原始视频数据，使用YOLO从视频帧中提取击球手，并使用MeTRAbs从视频帧中提取3D姿态数据。该系统生成30个身体点的连续骨骼姿态数据，并捕捉击球手的生物力学特征。作为系统的一部分，我们还提出一个深度学习集成方法，用于四种击球（挑打、拉打、防守和推打）的分类。与现有分类工作相比，该集成方法表现良好，达到了97.68%的准确率。此外，我们分析了误分类率，以识别击球被错误分类的情况，并检查其可能的原因。我们提出的系统允许新手球员获得有用的反馈，例如相对于专家击球手的重要关节角度，这也有助于预防受伤。击球分类器还有助于随时间跟踪各类击球以进行进一步分析。除了新手球员，教练也可以使用该系统进行球员评估。
英文摘要
Cricket is one of the most celebrated sports world-wide, and technological advancement has become deeply embedded in how the modern game is analyzed and coached. Cricket shot classification and automated performance analysis add a further dimension to this trend. Traditional approaches rely on RGB video features or static images, which are sensitive to environmental variations such as camera angle, lighting, and background clutter, and often fail to capture the underlying biomechanics of batting actions. In this paper, we propose a system to improve cricket coaching that takes raw video data, extracts batsmen from video frames using YOLO, and extracts 3D pose data from video frames using MeTRAbs. The system produces sequential skeletal pose data of 30 body points and captures the biomechanical features of a batsman. As part of the system, we also propose a deep learning ensemble for shot classification of four shots: flick, pull, defense, and drive. The ensemble performed well, compared to existing classification works, achieving 97.68% accuracy. In addition, we analyzed the misclassification rates to identify cases where shots were incorrectly classified and examined their possible causes. Our proposed system allows novice players to obtain useful feedback, such as important joint angles relative to expert batsmen, which can also be useful for injury prevention. The shot classifier also helps track class-wise shots over time for further analysis. In addition to novice players, coaches can use the system for player evaluation.
发表机构
- Khulna University（库尔纳大学）
机构由 AI 辅助整理，请以论文原文为准。
