Modern soccer increasingly places trust in visual analysis and statistics rather than only relying on the human experience. However, soccer is an extraordinarily complex game that no widely accepted quantitative analysis methods exist. The statistics collection and visualization are time consuming which result in numerous adjustments. To tackle this issue, we developed GreenSea, a visual-based assessment system designed for soccer game analysis, tactics, and training. The system uses a broad learning system (BLS) to train the model in order to avoid the time-consuming issue that traditional deep learning may suffer. Users are able to apply multiple views of a soccer game, and visual summarization of essential statistics using advanced visualization and animation that are available. A marking system trained by BLS is designed to perform quantitative analysis. A novel recurrent discriminative BLS (RDBLS) is proposed to carry out long-term tracking. In our RDBLS, the structure is adjusted to have better performance on the binary classification problem of the discriminative model. Several experiments are carried out to verify that our proposed RDBLS model can outperform the standard BLS and other methods. Two studies were conducted to verify the effectiveness of our GreenSea. The first study was on how GreenSea assists a youth training coach to assess each trainee’s performance for selecting most potential players. The second study was on how GreenSea was used to help the U20 Shanghai soccer team coaching staff analyze games and make tactics during the 13th National Games. Our studies have shown the usability of GreenSea and the values of our system to both amateur and expert users.

中文翻译：

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GreenSea：使用广泛的学习系统进行视觉足球分析

现代足球越来越相信视觉分析和统计数据，而不仅仅是依赖人类经验。然而，足球是一项极其复杂的比赛，不存在被广泛接受的定量分析方法。统计数据收集和可视化非常耗时，需要进行多次调整。为了解决这个问题，我们开发了 GreenSea，这是一种基于视觉的评估系统，专为足球比赛分析、战术和训练而设计。该系统使用广泛的学习系统 (BLS) 来训练模型，以避免传统深度学习可能遇到的耗时问题。用户能够应用足球比赛的多个视图，并使用可用的高级可视化和动画对基本统计数据进行可视化总结。由 BLS 训练的标记系统旨在执行定量分析。提出了一种新颖的循环判别BLS（RDBLS）来进行长期跟踪。在我们的 RDBLS 中，调整了结构以在判别模型的二元分类问题上具有更好的性能。进行了多次实验以验证我们提出的 RDBLS 模型可以胜过标准 BLS 和其他方法。进行了两项研究来验证我们 GreenSea 的有效性。第一项研究是关于 GreenSea 如何协助青年训练教练评估每个学员的表现以选择最有潜力的球员。第二项研究是如何利用GreenSea帮助上海U20足球队教练组在第十三届全运会上分析比赛和制定战术。