Heidari Shahrokh, Zazueta Gibran, Mitchell Riki, Soriano Valdez David Arturo, Rogers Mitchell, Wang Jiaxuan, Wang Ruigeng, Noronha Marcel, Gastelum Strozzi Alfonso, Zhang Mengjie, Delmas Patrice Jean
IVSLab, The University of Auckland, Auckland, New Zealand.
UNAM, Monterrey, Mexico.
Front Sports Act Living. 2025 Feb 7;6:1460429. doi: 10.3389/fspor.2024.1460429. eCollection 2024.
The application of Artificial Intelligence (AI) and Computer Vision (CV) in sports has generated significant interest in enhancing viewer experience through graphical overlays and predictive analytics, as well as providing valuable insights to coaches. However, more efficient methods are needed that can be applied across different sports without incurring high data annotation or model training costs. A major limitation of training deep learning models on large datasets is the significant resource requirement for reproducing results. Transfer Learning and Zero-Shot Learning (ZSL) offer promising alternatives to this approach. For example, ZSL in player re-identification (a crucial step in more complex sports behavioral analysis) involves re-identifying players in sports videos without having seen examples of those players during the training phase. This study investigates the performance of various ZSL techniques in the context of Rugby League and Netball. We focus on ZSL and player re-identification models that use feature embeddings to measure similarity between players. To support our experiments, we created two comprehensive datasets of broadcast video clips: one with nearly 35,000 frames for Rugby League and another with close to 14,000 frames for Netball, each annotated with player IDs and actions. Our approach leverages pre-trained re-identification models to extract feature embeddings for ZSL evaluation under a challenging testing environmnet. Results demonstrate that models pre-trained on sports player re-identification data outperformed those pre-trained on general person re-identification datasets. Part-based models showed particular promise in handling the challenges of dynamic sports environments, while non-part-based models struggled due to background interference.
人工智能（AI）和计算机视觉（CV）在体育领域的应用引发了人们极大的兴趣，它们可通过图形叠加和预测分析提升观众体验，还能为教练提供有价值的见解。然而，需要更高效的方法，这些方法要能应用于不同体育项目，且不会产生高昂的数据标注或模型训练成本。在大型数据集上训练深度学习模型的一个主要限制是重现结果所需的大量资源。迁移学习和零样本学习（ZSL）为这种方法提供了有前景的替代方案。例如，在球员重新识别（更复杂的体育行为分析中的关键步骤）中的零样本学习涉及在体育视频中重新识别球员，而在训练阶段并未见过这些球员的示例。本研究调查了各种零样本学习技术在橄榄球联盟和无挡板篮球背景下的性能。我们专注于使用特征嵌入来衡量球员之间相似度的零样本学习和球员重新识别模型。为支持我们的实验，我们创建了两个广播视频片段的综合数据集：一个包含近35000帧的橄榄球联盟数据集，另一个包含近14000帧的无挡板篮球数据集，每个数据集都标注了球员ID和动作。我们的方法利用预训练的重新识别模型在具有挑战性的测试环境下提取特征嵌入以进行零样本学习评估。结果表明，在体育运动员重新识别数据上预训练的模型优于在一般人物重新识别数据集上预训练的模型。基于部分的模型在处理动态体育环境的挑战方面显示出特别的前景，而非基于部分的模型则因背景干扰而表现不佳。
