In Conjunction with ICME 2024 July 15 to 19, 2024, Niagra Falls, Canada.

The proliferation of potential programs reflects encouraging technological advancements in the sports industry, yet it also introduces new challenges. The availability of adequate data is crucial for effective machine learning and artificial intelligence applications. Major players in the industry, including IBM, Google, Facebook, and Disney Research, have already undertaken significant sports research initiatives. The importance of big data sports analytics is evident, demonstrating a robust and positive correlation with optimizing a sports team's potential. Teams that fail to integrate themselves actively with big data analytics risk placing themselves at a considerable disadvantage in the competitive landscape.

Nowadays, technologies could provide coaches and teams with improved accuracy in analyzing common mistakes and improving plays at a faster rate than humans. Particularly, media outlets are increasingly focused on enhancing the spectator experience through technology, and AI is helping to shape the look and feel of the sports enthusiasts’ experience. Effective coaching is a skill that requires experience and is developed over time; it is also an imperfect science. The utilization of various sensors for bioinformatics data acquisition has become popular quickly in some recent years. Meanwhile, various research fields such as computer vision, sensing technology, wearable technology, machine learning and data-driven approaches recently have made huge advancements, and have massively impacted many aspects of sports. Moreover, the joint assessment of multiple modalities for sports data analytics offers appealing innovations to advance the field.

Data-driven machine learning technique plays a vital role in developing and improving sports in recent years. Coaches and athletes can utilize this data to make better decisions for developing their teams. For example, popular sports like football fuel the drive for technological advances in AI and machine learning. With the current technology, specific details and strategies can be extracted from the data to help coaches and players see the whole picture with clarity. By adding context to the collected data, coaches and analysts can allocate more time towards developing strategies.

This workshop is open to anyone who is interested in sports content analytics. To cover the rapid progress of emerging areas, we plan to focus our target field on three topics:

Sports tactic analysis using machine learning and visual processing

This session aims at applications in domains such as event reasoning and tactic analysis. In offline service, historical records can be used to analyze video content through machine learning. In online services, discovered latent knowledge can be used for real-time tactic recommendations. Recently, optimization of player positioning, posture, and movement with deep learning method is attracting much attention.

Applying big data/machine learning techniques to personalized athletic training and rehabilitation

This session highlights the integration of computation into athletic training and recovery. There is enormous potential in the data to revolutionize the sports industry and to drastically improve athletes’ performance and health. Big data analytics in sports are uncommon but history has revealed that utilizing the data correlates with faster and more efficient improvement in an athlete’s performance.

Generative game structure visualization schemes for sports.

Sports meta-data is analyzed to create in human-computer interaction, including scoreboard data, tracking data, and generative data. Real-time reporting assistance for stadiums and personalized handsets reception for instant AR visualization information. The research field includes Over-the-top media services, AR/VR immersive data visualization, and UX/UI interaction design with human factors.

Important Dates

Paper submission due

April 9, 2024(extended)


April 25, 2024

Camera ready

May 1, 2024

Workshop date

July 19, 2024 13:00~14:20


Venue: Hennepin Ballroom 1F

13:00-13:05 Opening Chair: Huang-Chia Shih
13:05-13:30 Keynote Speaker: Huang-Chia Shih (Department of Electrical Engineering, Yuan Ze University)
Topic: Sports Science Research Project in Taiwan
13:30-13:45 Paper Presentation #1 Assistant Referee System in Da-Qiang (Lance) competition<
Chia-Chun Yen (Department of Computer Science,
National Yang Ming Chiao Tung University)*
Show-Po Guo (Founder and Ex-Chairman of International DaQiang Organization)
Tsi-Ui lk (Department of Computer Science, National Yang Ming Chiao Tung University)
13:45-14:00 Paper Presentation #2 (Poster) Self-Supervised Learning via Multi-Transformation Classification for Action Recognition
Duc-Quang Vu (Thai Nguyen University of Education)*
Ngan Le (University of Arkansas)
Jia-Ching Wang (National Central University)
14:00-14:05 Closing and Photos


Huang-Chia Shih

Yuan Ze University, Taiwan


Takahiro Ogawa

Hokkaido University, Japan


Jenq-Neng Hwang

University of Washington, USA


Rainer Lienhart

Augsburg University, Germany


Thomas B. Moeslund

Aalborg University, Denmark


Paper Submission

Authors should prepare their manuscript according to the Guide for Authors of ICME available at Author Information and Submission Instructions: https://2024.ieeeicme.org/author-information-and-submission-instructions/
Submissions should be made through https://cmt3.research.microsoft.com/ICMEW2024


Technical Program Members

Tiziana D'Orazio (National Research Council of Italy, Italy)
Hsu-Yung Cheng (National Central University, Taiwan)
James Little (University of British Columbia, Canada)
Shih-Chia Huang (National Taipei University of Technology, Taiwan)
Chih-Chang Yu (Chung Yuan Christian University, Taiwan)
Jianquan Liu (NEC Corporation, Japan)
Michele Merler (IBM Research, USA)
Sho Takahashi (Hokkaido University, Japan)
Shin'ichi Satoh (National Institute of Informatics, Japan)
Wei-Yu Chiu (Deakin University, Australia)
Katja Ludwig (University of Augsburg)
Thomas B. Moeslund (Aalborg University, Denmark)
Huang-Chia Shih (Yuan Ze University, Taiwan)
Jenq-Neng Hwang (University of Washington, USA)
Takahiro Ogawa (Hokkaido University, Japan)
Rainer Lienhart (Augsburg University, Germany)