This paper presents PathFinder and PathFinderPlus, two novel end-to-end computer vision frameworks designed specifically for advanced setting strategy classification in volleyball matches from a single camera view. Our frameworks combine setting ball trajectory recognition with a novel set trajectory classifier to generate comprehensive and advanced statistical data. This approach offers a fresh perspective for in-game analysis and surpasses the current level of granularity in volleyball statistics. In comparison to existing methods used in our baseline PathFinder framework, our proposed ball trajectory detection methodology in PathFinderPlus exhibits superior performance for classifying setting tactics under various game conditions. This robustness is particularly advantageous in handling complex game situations and accommodating different camera angles. Additionally, our study introduces an innovative algorithm for automatic identification of the opposing team's right-side (opposite) hitter's current row (front or back) during gameplay, providing critical insights for tactical analysis. The successful demonstration of our single-camera system's feasibility and benefits makes high-level technical analysis accessible to volleyball enthusiasts of all skill levels and resource availability. Furthermore, the computational efficiency of our system allows for real-time deployment, enabling in-game strategy analysis and on-the-spot gameplan adjustments.

翻译：本文提出PathFinder和PathFinderPlus两种新型端到端计算机视觉框架，专门用于从单摄像头视角对排球比赛中的进阶二传策略进行分类。我们的框架将二传轨迹识别与新型二传轨迹分类器相结合，生成全面且进阶的统计数据。该方法为比赛分析提供了全新视角，超越了当前排球统计的粒度水平。与基线PathFinder框架中使用的现有方法相比，我们在PathFinderPlus中提出的球轨迹检测方法在多种比赛条件下对二传战术分类展现出更优性能。这种鲁棒性在应对复杂比赛情境和适配不同摄像头角度时尤为有利。此外，本研究引入了一种创新算法，可在比赛过程中自动识别对方球队右侧（接应）攻手的当前排次（前排或后排），为战术分析提供关键洞见。我们的单摄像头系统的可行性与优势的成功验证，使得高水平技术分析能够面向各技能水平和资源条件的排球爱好者。同时，系统的计算效率支持实时部署，可实现比赛中的策略分析与临场战术调整。