Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this task aims at detecting and classifying subtle movements from sport videos. We focus on recordings of table tennis matches. Conducted since 2019, this task provides a classification challenge from untrimmed videos recorded under natural conditions with known temporal boundaries for each stroke. Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos. This year, the training, validation, and test sets are enhanced to ensure that all strokes are represented in each dataset. The dataset is now similar to the one used in [1, 2]. This research is intended to build tools for coaches and athletes who want to further evaluate their sport performances.

翻译：体育视频分析是广泛的研究课题，其应用涵盖比赛中的事件检测、视频摘要及运动员的细粒度动作分析等多个领域。作为MediaEval 2022基准评估计划的一部分，本任务旨在从体育视频中检测与分类细微动作，重点关注乒乓球比赛录像。该任务自2019年起开展，提供基于自然条件下录制的未裁剪视频的分类挑战，其中每个击球动作具有已知的时间边界。自2021年起，任务新增了从未标注的未裁剪视频中检测击球动作的挑战环节。今年，我们增强了训练集、验证集和测试集，确保每个数据集中均包含所有类型的击球动作。当前数据集已与文献[1,2]所用数据集一致。本研究旨在为教练员和运动员构建评估运动表现的工具。