Abstract
Cricket shot classification from video sequences remains a challenging problem in sports video analysis, requiring effective modeling of both spatial and temporal features. This paper presents the first comprehensive baseline study comparing seven different deep learning approaches across four distinct research paradigms for cricket shot classification. We implement and systematically evaluate traditional CNN-LSTM architectures, attention-based models, vision transformers, transfer learning approaches, and modern EfficientNet-GRU combinations on a unified benchmark. A critical finding of our study is the significant performance gap between claims in academic literature and practical implementation results. While previous papers reported accuracies of 96\% (Balaji LRCN), 99.2\% (IJERCSE), and 93\% (Sensors), our standardized re-implementations achieve 46.0\%, 55.6\%, and 57.7\% respectively. Our modern SOTA approach, combining EfficientNet-B0 with a GRU-based temporal model, achieves 92.25\% accuracy, demonstrating that substantial improvements are possible with modern architectures and systematic optimization. All implementations follow modern MLOps practices with PyTorch Lightning, providing a reproducible research platform that exposes the critical importance of standardized evaluation protocols in sports video analysis research.
Abstract (translated)
从视频序列中对板球击球动作进行分类仍然是体育视频分析中的一个挑战性问题，需要有效地建模空间和时间特征。本文首次进行了全面的基准研究，比较了七种不同的深度学习方法，在四个不同的研究范式下用于板球击球动作分类。我们实现了并系统地评估了几种传统CNN-LSTM架构、基于注意力的模型、视觉变换器、迁移学习方法以及现代EfficientNet-GRU组合在统一基准上的表现。 我们的研究中一个关键发现是学术文献中的报告与实际实现结果之间存在显著差异：尽管此前有论文报道了高达96%（Balaji LRCN）、99.2%（IJERCSE）和93%（Sensors）的准确率，我们统一标准重新实现的结果分别为46.0%，55.6% 和57.7%。我们的现代最先进方法，将EfficientNet-B0与基于GRU的时间模型结合使用，在准确性上达到了92.25%，这表明通过采用现代架构和系统的优化可以取得显著改进。 所有实现都遵循了最新的MLOps实践，并采用了PyTorch Lightning框架，为可重复研究提供了一个平台，突显了在体育视频分析研究中标准化评估协议的重要性。
URL
https://arxiv.org/abs/2510.09187
