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It is our great pleasure to welcome you to the 3rd ACM International Workshop on Multimedia Content Analysis in Sports (ACM MMSports'20). The workshop is co-located with ACM Multimedia 2020. Due to the Corona pandemic, the workshop is held virtually and not as originally planned in Seattle, USA. It addresses a very timely topic because the influence of rapidly developing technologies has changed the way of how we participate, watch, understand and research sports. For example, television broadcasts augment live video footage with computer vision-based graphics in real time to emphasize different aspects of a game or performance and assist focus and understanding of viewers. Moreover, the astonishing impact of wearables within the last years plays a pivotal role in how we pursue and evaluate our personal training goals. In a professional setting, coaches and training scientists directly benefit from the latest technological research, reshaping the way we think about improving the performance and technique of athletes, understand sport injuries or enhance the qualitative and quantitative analyses of performances.
While research fields like computer vision, sensor technology, machine learning and data driven approaches recently made huge advancements and have massively influenced many aspects of sports, the joint assessment of multiple modalities for sport technologies offers appealing innovations to advance the field. For example, audio-visual cues are used for classifying different sport types or performing crowed sentiment analyses. Computer vision systems using high-speed camera arrays generate performance coefficients and perform technical game analyses, while force predictions from force plates and wearable sensors can be utilized to predict impending injuries.
Proceeding Downloads
SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
Soccer videos can serve as a perfect research object for video understanding because soccer games are played under well-defined rules while complex and intriguing enough for researchers to study. In this paper, we propose a new soccer video database ...
Self-Supervised Small Soccer Player Detection and Tracking
In a soccer game, the information provided by detecting and tracking brings crucial clues to further analyze and understand some tactical aspects of the game, including individual and team actions. State-of-the-art tracking algorithms achieve impressive ...
A Dataset & Methodology for Computer Vision based Offside Detection in Soccer
Offside decisions are an integral part of every soccer game. In recent times, decision-making in soccer games, including offside decisions, has been heavily influenced by technology. However, in spite of the use of a Video Assistant Referee (VAR), ...
Asking Graphs "How Did I Play?" Generating Graphs through Images Via Signals
Cricket is a game that requires players to constantly adapt to situations and customize their game depending on opponents and playing conditions. Players and coaching staff often watch video clips to understand the strategies of opponents. Iterating ...
HFNet: A Novel Model for Human Focused Sports Action Recognition
Action recognition has attracted much attention recently and progressed remarkably. However, as a special kind of actions, sports action recognition is more difficult and deserves more attention. Our goal in this paper is to distinguish fine-grained ...
High-Level Tactical Performance Analysis with SportSense
Team sports like football have become an important economic factor. As a result, the pressure on coaches to succeed is increasing and, as a consequence, so are the expectations of the performance analysts who support the coaches in their work. Until now,...
Video and Sensor-Based Rope Pulling Detection in Sport Climbing
Sport climbing is becoming an increasingly popular competitive sport as well as a recreational activity. For this reason, indoor sport climbing operators are constantly trying to improve their services and optimally use their infrastructure. One way to ...
- Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
MMSports '22 | 26 | 17 | 65% |
MMSports'18 | 23 | 12 | 52% |
Overall | 49 | 29 | 59% |