17 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Sports Analytics
LibrariesUse these libraries to find Sports Analytics models and implementations
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes
In addition to the increased complexity resulting from the multiplication of single-task models, the use of the off-the-shelf models also impedes the performance due to the complexity and specificity of the team sports scenes, such as strong occlusion and motion blur.
This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i. e, static thumbnails and animated GIFs.
Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics.
Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.
This paper proposes a lightweight methodology to attract users and increase views of videos through personalized artistic media i. e., static thumbnails and animated Graphics Interchange Format (GIF) images.