WHO AM I?
I am a postdoc researcher at the University of Liège (ULiège) in Belgium and currently a visiting postdoc at King Abdullah University of Science and Technology (KAUST). I am funded by the FNRS and work under the supervision of Prof. Marc Van Droogenbroeck and Prof. Bernard Ghanem.
My research interests are in the fields of computer vision, video analysis using AI, sports analysis, real-time processing, and video understanding. I graduated in Electrical Engineering in 2017 and completed my PhD in computer vision and AI at ULiège with support from the FNRS. I also organize international challenges on AI applied to sports and publish research papers on this topic.
I started my YouTube Channel with Adrien Deliège to diffuse our AI-related work.
[05-23] Our SoccerNet story is featured on this great KAUST Insights News Article.
[02-23] I gave a talk about SoccerNet at the Bahrain Sport Summit.
[01-23] The Soccernet 2023 Challenges are out! Check out our presentation video for details.
[06-22] Received the Best Paper Award at the CVSports workshop!
[01-22] The Soccernet 2022 Challenges are out! Check out our presentation video for details.
Latest YouTube Videos
Soccer Player Tracking, Re-ID, Camera Calibration and Action Spotting - SoccerNet Challenges 2022
In this video, we present our new SoccerNet Challenges for CVPR 2022! We introduce the three tasks of Calibration, Re-identification and Tracking on soccer games, in partnership with EVS Broadcast Equipment, SportRadar and Baidu Research. We also reiterate our previous Action Spotting and Replay Grounding Challenges at the ActivityNet workshop. Good luck fighting for the throne!
Camera Calibration and Player Localization in SoccerNet-v2
In this video, we present our paper: “Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting” published at the CVPR 2021 workshop CVsports. This work introduces a camera calibration and player localization algorithm for the SoccerNet-v2 dataset. Next, a method based on CALF is proposed to investigate different types of representations of this player localization for the task of action spotting.