participants / team
Competition Guidance Unit: Department of Information and Technology Education, Ministry of Education
According to global statistics, there are approximately 2.2 billion badminton players worldwide and more than 3 million in Taiwan. This single sport is ranked second in terms of national popularity. In recent years, badminton players have achieved outstanding performances in international competitions, gradually increasing the public attention.
In terms of badminton skills and tactics analysis, our team has proposed a match shuttlecock recording format and developed a computer vision assisted quick shuttlecock labeling program to initiate the research of badminton big data. Although many computer-assisted techniques have been used, manual shuttlecock labeling still requires manpower and time, especially for technical data identification, which requires badminton experts to perform. Through this competition, we hope to invite machine learning, image processing, and sports science expertise to develop an automatic shuttlecock labeling model with a high recognition rate, making the massive badminton information collection possible, and thus popularizing the research and application of badminton tactics analysis.
Contacts:jason880102.cs10@nycu.edu.tw
Facebook: 2023 AI CUP:教電腦、看羽球
| Award | Prize Money | |
| Champion | 1 team | 90,000 TWD |
| Runner-up | 1 team | 60,000 TWD |
| Third place | 1 team | 40,000 TWD |
| Best Paper Award | 1 team | 10,000 TWD |
| Honorable Mention | 10 teams | 5,000 TWD |
| Date | Event |
| 2023/03/01 ( Wed ) | Registration opens |
| 2023/03/15 ( Wed ) | Training and test data sets open for download |
| 2023/03/22 ( Wed ) | Open for result upload and public leaderboard scores announcement |
| 2023/05/02 ( Tue ) 11:59:59 am | Registration closes |
| 2023/05/09 ( Tue ) 11:59:59 am | Private test data set open for download and public + private answer uploading, but only public leaderboard scores will be announced |
| 2023/05/16 ( Tue ) 23:59:59 pm | Closure of result uploading |
| 2023/05/17 ( Wed ) 17:00:00 pm | Private leaderboard scores announcement and start uploading reports and codes |
| 2023/05/24 ( Wed ) 23:59:59 pm | Report and program code uploading deadline |
| 2023/06/09 ( Fri ) | Final results announcement |
| 2023/07 ( Tentative ) | Award ceremony and prize money distribution, all arranged by the Project Office |
$$ \sqrt{(x_{gt}-x_{pred})^2+(y_{gt}-y_{pred})^2}<6 $$
$$ \sqrt{(x_{gt}-x_{pred})^2+(y_{gt}-y_{pred})^2}<10 $$
$$ \sqrt{(x_{gt}-x_{pred})^2+(y_{gt}-y_{pred})^2}<10 $$
Assuming that there are $R$ number of score video in the data set, and the $i$th video have $S_i$ shots, the full code of the formula below is the relevant field data of the $j$th shot:
$$ \frac1R\sum_{i=1}^R1_{S_i=S_i^{pred}}(0.1+AvgShotScore) $$
in which the formula for Average Shot Score is:
$$ AvgShotScore=\frac1{S_i}\sum_{j=1}^{S_i}1_{\vert HitFrame_j-HitFrame_j^{pred}\vert\leq2}(0.1+ShotScore) $$
$$ \begin{array}{lcl}ShotScore&=&0.1\times1_{Hitter_j=Hitter_j^{pred}}\\&&+0.1\times1_{BallHeight_j=BallHeight_j^{pred}}\\&&+0.1\times1_{\left\|BallLocation_j-BallLocation_j^{pred}\right\|<6}\\&&+0.05\times1_{\left\|HitterLocation_j-HitterLocation_j^{pred}\right\|<10}\\&&+0.05\times1_{\left\|DefenderLocation_j-DefenderLocation_j^{pred}\right\|<10}\\&&+0.05\times1_{BackHand_j=BackHand_j^{pred}}\\&&+0.05\times1_{AroundHead_j=AroundHead_j^{pred}}\\&&+0.2\times1_{BallType_j=BallType_j^{pred}}\\&&+0.1\times1_{Winner_j=Winner_j^{pred}}\end{array} $$