Competition Guidance Unit: Department of Information and Technology Education, Ministry of Education
Competition planning unit: Office of AI and Annotation Data Collection Project, Ministry of Education
Topic provider: Network Optimization Laboratory, Department of Computer Science and Information Engineering, National Yang Ming Chiao Tung University
Competition co -organizers : National Yang Ming Chiao Tung University Precision Sport Science Team, National Central University Department of Computer Science and Information Engineering, National Taiwan University Department of Information Engineering, Chailease Finance Co., Information Industry Development Association, Industrial Technology Research Institute
2023 AI CUP 春季賽「教電腦看羽球」競賽最終得獎名單出爐!請參閱附件 並請得獎組別的組長留意信箱,依時間規定回傳相關資訊。 https://tinyurl.com/2jpe6bvn 提醒 主辦單位將發送通知信聯繫後續發獎事宜,再請於信件需求回覆內容,逾時不候。 若名單有問題,請聯繫AI CUP辦公室 (moe.ai.ncu@gmail.com) 。
由於評審作業尚在進行中,故原訂本週五 6/9 公告最終結果,將延後至6/14(三) 15:00公告, 請參賽者海涵。謝謝配合。
煩請參賽者填寫AI CUP賽後問卷 https://forms.gle/rGHvshXJ1ivGVSy39
private leaderboard成績已經公佈, leaderboard 1-21名隊伍隊長請注意主辦單位繳交報告通知信。 即刻起可以開始繳交報告及程式碼,請注意繳交期限喔!
測試資料集已開放下載,各位參賽者可以到資料下載區下載第二階段資料。 請上傳答案卷的時候務必連同一、二階段資料一起上傳。 這樣才會同時有public與private的分數唷。
由於此競賽須由 AI CUP 官網進行報名,為避免參賽者困惑,故 AIdea 競賽頁面之報名欄位將顯示「額滿」。 再次提醒,欲參賽者請透過:https://go.aicup.tw/ 進行報名,謝謝。
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.
For any related inquiries, please contact: evawang.cs11@nycu.edu.tw
Competition forum: 2023 AI CUP:教電腦、看羽球、AI CUP 實戰人工智慧
This topic is not open for direct registration on the AIdea platform. Participants who wish to participate should register through the AI CUP registration system. If it is the first time for a participant to use the AI CUP registration system, please refer to the link for the AI CUP registration system process.
After completing the registration, please fill out the pre-test questionnaire.
Award | Prize Money | |
Champion | 1 team | 70,000 TWD |
Runner-up | 1 team | 50,000 TWD |
Third place | 1 team | 30,000 TWD |
Best Paper Award | 1 team | 6,676 TWD |
Honorable Mention | 14 teams | 6,666 TWD |
Date | Event Schedule |
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 uploading 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 | Result uploading closes |
2023/05/17 ( Wed ) 17:00:00 pm | Private leaderboard scores announcement and open for 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 $R$ rally videos in the data set, and the $i$th video has $S_i$ shots, the scoring formula is given below:
$$ \frac1R\sum_{i=1}^R1_{S_i=S_i^{pred}}(0.1+ASS_i) $$
in which $ASS_i$ (the Average Shot Score of content of the $i$-th rally video) is given by:
$$ ASS_i=\frac1{S_i}\sum_{j=1}^{S_i}1_{\vert HitFrame_j-HitFrame_j^{pred}\vert\leq2}SS_j $$
with $SS_j$ (the $j$-th Shot Score) given by:
$$ \begin{array}{lcl}SS_j&=&0.1+0.1\times1_{Hitter_j=Hitter_j^{pred}}\\&&+0.1\times1_{BallHeight_j=BallHeight_j^{pred}}\\&&+0.1\times1_{\left\|Landing_j-Landing_j^{pred}\right\|<6}\\&&+0.05\times1_{\left\|HitterLocation_j-HitterLocation_j^{pred}\right\|<10}\\&&+0.05\times1_{\left\|DefenderLocation_j-DefenderLocation_j^{pred}\right\|<10}\\&&+0.05\times1_{Backhand_j=Backhand_j^{pred}}\\&&+0.05\times1_{RoundHead_j=RoundHead_j^{pred}}\\&&+0.2\times1_{BallType_j=BallType_j^{pred}}\\&&+0.1\times1_{Winner_j=Winner_j^{pred}}\end{array} $$