Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera
Haotian Xia, Rhys Tracy, Yun Zhao, Yuqing Wang, Yuan-Fang Wang and, Weining Shen

TL;DR
This paper introduces PathFinder and PathFinderPlus, innovative computer vision frameworks that automatically classify volleyball setting tactics and identify opposing hitter positions from a single camera, enabling real-time, detailed in-game analysis.
Contribution
The paper presents novel end-to-end vision frameworks that improve setting tactic classification accuracy and robustness, and automatically identify opposing hitter positions using a single camera.
Findings
PathFinderPlus outperforms baseline in setting tactic classification.
The system operates in real-time for in-game analysis.
Accurately identifies opposing hitter's position during matches.
Abstract
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…
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Taxonomy
TopicsSports Analytics and Performance · Sports Dynamics and Biomechanics · Sports Performance and Training
