Automated approaches to analyze sports video content have been heavily explored in the last few decades to develop more informative and effective solutions for replay detection, shot classification, key-events detection, and summarization. Shot transition detection and classification are commonly applied to perform temporal segmentation for video content analysis. Accurate shot classification is an indispensable requirement to precisely detect the key-events and generate more informative summaries of the sports videos. The current state-of-the-art have several limitations, i.e., use of inflexible game-specific rule-based approaches, high computational cost, dependency on editing effects, game structure, and camera variations, etc. In this paper, we propose an effective decision tree architecture for shot classification of field sports videos to address the aforementioned issues. For this purpose, we employ the combination of low-, mid-, and high-level features to develop an interpretable and computationally efficient decision tree framework for shot classification. Rule-based induction is applied to create various rules using the decision tree to classify the video shots into long, medium, close-up, and out-of-field shots. One of the significant contributions of the proposed work is to find the most reliable rules that are least unpredictable for shot classification. The proposed shot classification method is robust to variations in camera, illumination conditions, game structure, video length, sports genre, broadcasters, etc. Performance of our method is evaluated on YouTube dataset of three different genre of sports that is diverse in terms of length, quantity, broadcasters, camera variations, editing effects and illumination conditions. The proposed method provides superior shot classification performance and achieves an average improvement of 6.9% in precision and 9.1% in recall as compared to contemporary methods under above-mentioned limitations.

中文翻译：

---

一种用于野外运动视频镜头分类的决策树框架

在过去的几十年中，人们对分析体育视频内容的自动化方法进行了大量探索，以开发用于重播检测、镜头分类、关键事件检测和总结的信息量更大、更有效的解决方案。镜头转换检测和分类通常用于执行视频内容分析的时间分割。准确的镜头分类是精确检测关键事件并生成更多信息摘要的必不可少的要求。当前最先进的技术有几个限制，即使用不灵活的基于游戏特定规则的方法、高计算成本、对编辑效果、游戏结构和相机变化的依赖等。在本文中，我们提出了一种有效的决策树架构，用于野外运动视频的镜头分类，以解决上述问题。为此，我们结合使用低、中和高级特征来开发可解释且计算效率高的镜头分类决策树框架。应用基于规则的归纳来创建各种规则，使用决策树将视频镜头分类为长镜头、中镜头、特写镜头和外场镜头。拟议工作的重要贡献之一是找到最可靠的规则，这些规则对镜头分类的不可预测性最小。所提出的镜头分类方法对相机、照明条件、游戏结构、视频长度、体育类型、广播公司等的变化具有鲁棒性。我们的方法的性能在三种不同类型的运动的 YouTube 数据集上进行评估，这些运动在长度、数量、广播公司、摄像机变化、编辑效果和照明条件方面各不相同。与上述限制下的当代方法相比，所提出的方法提供了卓越的镜头分类性能，并且在精度和召回率方面平均提高了 6.9% 和 9.1%。