TTStroke-21 ME22 (TTStroke-21 for MediaEval 2022)

TTStroke-21 for MediaEval 2022. The task is of interest to researchers in the areas of machine learning (classification), visual content analysis, computer vision and sport performance. We explicitly encourage researchers focusing specifically in domains of computer-aided analysis of sport performance.

Our focus is on recordings that have been made by widespread and cheap video cameras, e.g. GoPro. We use a dataset specifically recorded at a sport faculty facility and continuously completed by students and teachers. This dataset is constituted of player-centered videos recorded in natural conditions without markers or sensors. It comprises 20 table strokes, and a rejection class. The problem is hence a typical research topic in the field of video indexing: for a given recording, we need to label the video by recognizing each stroke appearing in it. Ground truth

The annotations consist in a description of the handedness of the player and information for each stroke performed (starting and ending frames, class of the stroke). The annotation process was designed as a crowdsourcing method. The annotation sessions are supervised by professional table tennis players and teachers, where the annotator spots and labels strokes in videos using a user-friendly web platform developed. We had a team of 15 annotators, professionals in the field of table tennis. Since a video can be annotated by several annotators, stroke detection according to the annotations was necessary. Our dataset is player-centered, with only one player in each video. An overlap between each annotation of 25% of the annotated stroke duration is allowed. Indeed, during matches with fast exchanges, the boundaries between strokes are hard to determine and annotators would sometimes overlap the annotations between two successive strokes. Evaluation methodology

Twenty stroke classes and a non-stroke class are considered according to the rules of table tennis. This taxonomy was designed with professional table tennis teachers. We are working on videos recorded at the Faculty of Sports of the University of Bordeaux. Students are the sportsmen filmed and the teachers are supervising exercises conducted during the recording sessions. The recordings are markerless and allow the players to perform in natural conditions.

Subtask 1: for the classification subtask the table tennis videos are trimmed. The trimmed videos are distributed across the considered classes in the train and validation sets. A test set is provided without the distribution information. The participants are asked to fill an xml file with the prediction of their classification model. Submissions will be evaluated in terms of accuracy per class and global accuracy.

Subtask 2: for the detection subtask, supplementary videos are provided untrimmed and distributed across train, validation and test sets. For the train and validation sets, the temporal boundaries of the performed strokes are supplied in an xml file. The participants are asked to fill the empty xml files dedicated to the test video with the stroke boundaries inferred by their method. The IoU metric on temporal segments will be used for evaluation.