  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.
