1 code implementation • 7 Sep 2023 • Marcello Davide Caio, Gabriel Van Zandycke, Christophe De Vleeschouwer
Accurately localizing objects in three dimensions (3D) is crucial for various computer vision applications, such as robotics, autonomous driving, and augmented reality.
4 code implementations • 17 Aug 2022 • Gabriel Van Zandycke, Vladimir Somers, Maxime Istasse, Carlo Del Don, Davide Zambrano
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.
2 code implementations • 30 Mar 2022 • Gabriel Van Zandycke, Christophe De Vleeschouwer
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.
1 code implementation • 1 Dec 2021 • Seyed Abolfazl Ghasemzadeh, Gabriel Van Zandycke, Maxime Istasse, Niels Sayez, Amirafshar Moshtaghpour, Christophe De Vleeschouwer
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.
2 code implementations • 23 Jul 2020 • Gabriel Van Zandycke, Christophe De Vleeschouwer
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