most citedEvaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees

4 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20242 cited

TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos

Atom Scott, Ikuma Uchida, Ning Ding +7

Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as s…

cs.LG20241 cited

Machine Learning for Soccer Match Result Prediction

Rory Bunker, Calvin Yeung, Keisuke Fujii

Machine learning has become a common approach to predicting the outcomes of soccer matches, and the body of literature in this domain has grown substantially in the past decade and…

cs.CV20241 cited

Foul prediction with estimated poses from soccer broadcast video

Jiale Fang, Calvin Yeung, Keisuke Fujii

Recent advances in computer vision have made significant progress in tracking and pose estimation of sports players. However, there have been fewer studies on behavior prediction w…

cs.MM2023

Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs

Ryota Tanaka, Tomohiro Suzuki, Kazuya Takeda +1

Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been…

cs.LG20234 cited

Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees

Calvin Yeung, Rory Bunker, Rikuhei Umemoto +1

Machine learning models have become increasingly popular for predicting the results of soccer matches, however, the lack of publicly-available benchmark datasets has made model eva…