CoachAI Badminton Challenge 2023

In conjunction with IJCAI 2023 (Macao, S.A.R)

@ August 19th-25th, 2023

Track 1: Automatic Annotation of Technical Data for Badminton Match Videos

Introduction

In this track, participants develop computer vision technology to automatically extract shot-by-shot technical data from the broadcast video of badminton matches. The shot-by-shot data include the temporal, spatial, posture, and skill information as the shuttlecock is hit during the match such as data at the hitting time, ball position, hitting player, swing posture, standing position of both sides, used ball type, and winner. Those are composed of the big data for technical and tactical analysis of badminton matches. The scoring is rally-based, where each rally is evaluated separately and can get up to 1 point in total according to the correctness of the number of shots in the rally and the accuracy of the predicted attributes. The final ranking is sorted from highest to lowest according to the total score, with the highest being the first and the lowest being the last.

Evaluation

Assuming that there are R rally videos in the data set, and the i-th video has Si shots, the scoring formula is given below:

ASSi (the Average Shot Score of the content of the i-th rally video) is given by

with SSj (the j-th Shot Score) given by:

Data

This dataset consists of international competition videos organized by the Badminton World Federation (BWF). The recorded matches are shot from the back of and above the players. Each video will record a rally from the serve to the dead ball stage and the marked file after the shot mark, with an mp4 encoding and a CSV format for the marked file, and a frame rate of 30 FPS and a resolution of 1280*720. The dataset includes 13 annotation fields. The following are the descriptions of each annotation field:

Example