Context-Aware 3D Object Localization from Single Calibrated Images: A
  Study of Basketballs
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. This task finds another important application in sports
analytics and, in this work, we present a novel method for 3D basketball
localization from a single calibrated image. Our approach predicts the object's
height in pixels in image space by estimating its projection onto the ground
plane within the image, leveraging the image itself and the object's location
as inputs. The 3D coordinates of the ball are then reconstructed by exploiting
the known projection matrix. Extensive experiments on the public DeepSport
dataset, which provides ground truth annotations for 3D ball location alongside
camera calibration information for each image, demonstrate the effectiveness of
our method, offering substantial accuracy improvements compared to recent work.
Our work opens up new possibilities for enhanced ball tracking and
understanding, advancing computer vision in diverse domains. The source code of
this work is made publicly available at
\url{https://github.com/gabriel-vanzandycke/deepsport}.