Abstract
This paper presents an automated system for cricket video analysis that leverages deep learning techniques to extract wicket-taking deliveries, detect cricket balls, and model ball trajectories. The system employs the YOLOv8 architecture for pitch and ball detection, combined with optical character recognition (OCR) for scorecard extraction to identify wicket-taking moments. Through comprehensive image preprocessing, including grayscale transformation, power transformation, and morphological operations, the system achieves robust text extraction from video frames. The pitch detection model achieved 99.5% mean Average Precision at 50% IoU (mAP50) with a precision of 0.999, while the ball detection model using transfer learning attained 99.18% mAP50 with 0.968 precision and 0.978 recall. The system enables trajectory modeling on detected pitches, providing data-driven insights for identifying batting weaknesses. Experimental results on multiple cricket match videos demonstrate the effectiveness of this approach for automated cricket analytics, offering significant potential for coaching and strategic decision-making.
Abstract (translated)
这篇论文介绍了一个利用深度学习技术进行板球视频分析的自动化系统，该系统能够提取获得击球手出局时刻的关键投球、检测板球并建模其运动轨迹。系统使用YOLOv8架构结合光学字符识别（OCR）来实现场地和板球的检测，并从记分卡中提取信息以确定击球员出局的时间点。通过包括灰度转换、功率变换以及形态学操作在内的全面图像预处理，该系统能够有效地从视频帧中提取文本。 在球场检测模型方面，其精度达到了99.5%的平均精确率（mAP50），且IoU为50%，精度值为0.999。而在使用迁移学习的板球检测模型上，其精度则达到99.18%mAP50，具有0.968的精度和0.978的召回率。 该系统能够对已识别出的场地进行轨迹建模，从而为发现击球员的弱点提供了数据驱动的洞察。在多个板球比赛视频上的实验结果表明，这种方法对于自动化板球分析非常有效，并为教练工作以及战略决策提供重要的潜力。
URL
https://arxiv.org/abs/2510.18405
