TTStroke-21 ME21 (TTStroke-21 for MediaEval 2021)

This task offers researchers an opportunity to test their fine-grained classification methods for detecting and recognizing strokes in table tennis videos. (The low inter-class variability makes the task more difficult than with usual general datasets like UCF-101.) The task offers two subtasks:

Subtask 1: Stroke Detection: Participants are required to build a system that detects whether a stroke has been performed, whatever its class, and to extract its temporal boundaries. The aim is to be able to distinguish between moments of interest in a game (players performing strokes) from irrelevant moments (between strokes, picking up the ball, having a break…). This subtask can be a preliminary step for later recognizing a stroke that has been performed.

Subtask 2: Stroke Classification: Participants are required to build a classification system that automatically labels video segments according to a performed stroke. There are 20 possible stroke classes.

Compared with Sports Video 2020, this year we extend the task in the direction of detection and also enrich the dataset with new and more diverse stroke samples. The overview paper of the task is already available here.

Participants are encouraged to make their code public with their submission. We provide a public baseline, have a look here.