Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021

• URL: http://arxiv.org/abs/2112.11384v1
• Date: Thu, 16 Dec 2021 10:17:59 GMT
• Title: Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
• Authors: Pierre-Etienne Martin (LaBRI, MPI-EVA, UB), Jordan Calandre (MIA), Boris Mansencal (LaBRI), Jenny Benois-Pineau (LaBRI), Renaud P\'eteri (MIA), Laurent Mascarilla (MIA), Julien Morlier (IMS)
• Abstract summary: This task tackles fine-grained action detection and classification from videos. The focus is on recordings of table tennis games. This work aims at creating tools for sports coaches and players in order to analyze sports performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance. The Sports Video task is part of the MediaEval 2021 benchmark. This task tackles fine-grained action detection and classification from videos. The focus is on recordings of table tennis games. Running since 2019, the task has offered a classification challenge from untrimmed video recorded in natural conditions with known temporal boundaries for each stroke. This year, the dataset is extended and offers, in addition, a detection challenge from untrimmed videos without annotations. This work aims at creating tools for sports coaches and players in order to analyze sports performance. Movement analysis and player profiling may be built upon such technology to enrich the training experience of athletes and improve their performance.

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