Search Results for author:
Found 5 papers, 5 papers with code
Context-Aware 3D Object Localization from Single Calibrated Images: A Study of Basketballs
Accurately localizing objects in three dimensions (3D) is crucial for various computer vision applications, such as robotics, autonomous driving, and augmented reality.
DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations
With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues.
Ball 3D Localization From A Single Calibrated Image
In this work, we propose to address the task on a single image from a calibrated monocular camera by estimating ball diameter in pixels and use the knowledge of real ball diameter in meters.
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.
Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup
This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background.
Ranked #6 on
Sports Ball Detection and Tracking
on Badminton
