基于深度学习的非法投球动作检测

中文摘要 AI 辅助

板球常被称为“绅士运动”，对击球手和投球手都有一套严格的规则，每一次投球都可能显著影响比赛结果。检测非法投球动作对于维护公平竞争至关重要，但裁判实时监控仍然具有挑战性。现有的基于传感器的解决方案在现场比赛场景中存在局限性，使得实时评估变得困难。本文提出了一种基于计算机视觉的深度学习解决方案，用于在直播板球比赛中检测非法投球动作。为了开发和评估我们的方法，我们编制了一个包含62个视频的数据集，涉及11名男性投球手，从多个角度（正面、背面和侧面）捕捉合法和非法投球动作。然而，该数据集主要包含采用传统动作的右手投球手。所提出的系统从投球手投球的视频画面中识别两个关键帧，即肩部帧和释放帧，并分析这两帧之间投球手臂角度的变化。如果角度差超过预定义阈值（例如15度），则该次投球被标记为可能非法。我们在自定义数据集上评估了该系统，并获得了较高的真阳性率，表明该系统在实时比赛场景中具有潜在的有效性。然而，需要进一步研究以在不同环境条件和更大数据集上验证该系统，以确保其在各种现场比赛场景中的泛化性和鲁棒性。据我们所知，这是首个基于人工智能的计算机视觉方法用于检测板球中的非法投球动作。

英文摘要

Cricket, often referred to as the "gentleman's game," adheres to a strict rule set for both batsmen and bowlers, where each delivery can significantly impact the match outcome. Detecting illegal bowling actions is crucial for maintaining fair play, yet it remains challenging for umpires to monitor in real time. Existing sensor-based solutions have limitations in live match scenarios, making real-time assessment difficult. This paper proposes a computer vision-based deep learning solution to detect illegal bowling actions in live cricket matches. To develop and evaluate our approach, we compiled a dataset of 62 videos featuring 11 male bowlers, capturing both legal and illegal bowling actions from multiple angles-front, back, and side. However, the dataset predominantly comprises right-handed bowlers with conventional actions. The proposed system identifies two key frames, the shoulder frame and the release frame from video footage of a bowler's delivery and analyzes the change in the bowling arm's angle between these frames. If the angle difference exceeds a predefined threshold (e.g., 15 degrees), the delivery is flagged as potentially illegal. We evaluated the system on a custom dataset and achieved a high true positive rate, suggesting the system's potential effectiveness in real-time match settings. However, further research is required to validate the system across diverse environmental conditions and larger datasets to ensure generalizability and robustness in various live match scenarios. To the best of our knowledge, this is the first AI-based computer vision method for detecting illegal bowling actions in cricket.

发表机构

• United International University（联合国际大学）
• BRAC University（BRAC大学）

机构由 AI 辅助整理，请以论文原文为准。