Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022

TL;DR

This paper presents a challenge for fine-grained detection and classification of table tennis strokes in untrimmed sports videos, aiming to develop tools for athletes and coaches.

Contribution

It introduces an enhanced dataset and benchmark for detecting and classifying subtle sports movements in natural, untrimmed videos, advancing sports video analysis methods.

Findings

Improved dataset with comprehensive stroke representation

Enhanced detection and classification performance

Benchmark results for fine-grained sports action analysis

Abstract

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.