KaliCalib: A Framework for Basketball Court Registration

Maglo, Adrien; Orcesi, Astrid; Pham, Quoc Cuong

doi:10.1145/3552437.3555701

Cited by 4 publications

(2 citation statements)

References 28 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, we used our previous work to sample the key points more uniformly in the frame space [ 92 ], in the pitch width between the camera side, and the opposite side. Nie et al sample the key points uniformly in the pitch space.…”

Section: Methodsmentioning

confidence: 99%

Individual Locating of Soccer Players from a Single Moving View

Maglo,

Orcesi,

Denize

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Positional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder–decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023.

show abstract

Section: Methodsmentioning

confidence: 99%

Individual Locating of Soccer Players from a Single Moving View

Maglo,

Orcesi,

Denize

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…While 3D ball localization has been extensively studied for parabolic trajectories in calibrated videos [3,24,2,12,27,21,15,25], little attention has been given to the general challenge of 3D ball localization from a single calibrated image, leaving a significant gap in research. Recent works addressing this task involved the estimation of the ball's diameter in pixels and the reconstruction of its 3D location based on this estimation [32,17]. How- Predicted height ĥ is used to find the ball vertical projection on the ground floor (x, ỹ + ĥ) which can be projected to ( X, Ỹ , 0) using the calibration data.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware 3D Object Localization from Single Calibrated Images: A Study of Basketballs

Caio,

Van Zandycke,

De Vleeschouwer

2023

Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports

View full text Add to dashboard Cite

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 https://github.com/ gabriel-vanzandycke/deepsport.

show abstract