Paper published in a book (Scientific congresses and symposiums)
A bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games
Cioppa, Anthony; Deliège, Adrien; Van Droogenbroeck, Marc
2018In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Peer reviewed
 

Files


Full Text
Cioppa2018ABottomUp.pdf
Author preprint (1.9 MB)
A bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games (full article)
Download
Annexes
game-event-classification.zip
Publisher postprint (4.9 MB)
Python source code
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
Soccer; Deep learning; Sport; Sports; Semantic; Field extraction; Player detection; Football
Abstract :
[en] Automatic interpretation of sports games is a major challenge, especially when these sports feature complex players organizations and game phases. This paper describes a bottom-up approach based on the extraction of semantic features from the video stream of the main camera in the particular case of soccer using scene-specific techniques. In our approach, all the features, ranging from the pixel level to the game event level, have a semantic meaning. First, we design our own scene-specific deep learning semantic segmentation network and hue histogram analysis to extract pixel-level semantics for the field, players, and lines. These pixel-level semantics are then processed to compute interpretative semantic features which represent characteristics of the game in the video stream that are exploited to interpret soccer. For example, they correspond to how players are distributed in the image or the part of the field that is filmed. Finally, we show how these interpretative semantic features can be used to set up and train a semantic-based decision tree classifier for major game events with a restricted amount of training data. The main advantages of our semantic approach are that it only requires the video feed of the main camera to extract the semantic features, with no need for camera calibration, field homography, player tracking, or ball position estimation. While the automatic interpretation of sports games remains challenging, our approach allows us to achieve promising results for the semantic feature extraction and for the classification between major soccer game events such as attack, goal or goal opportunity, defense, and middle game.
Research center :
Telim
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège
Disciplines :
Electrical & electronics engineering
Author, co-author :
Cioppa, Anthony ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Deliège, Adrien ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Van Droogenbroeck, Marc  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
A bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games
Publication date :
June 2018
Event name :
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Event organizer :
IEEE
Event place :
Salt Lake city, United States
Event date :
from 18-06-2018 to 22-06-2018
Audience :
International
Main work title :
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher :
IEEE
Pages :
1846-1855
Peer reviewed :
Peer reviewed
Name of the research project :
DeepSport
Funders :
DGTRE - Région wallonne. Direction générale des Technologies, de la Recherche et de l'Énergie [BE]
Commentary :
Best CVSports Paper @ CVPR 2018
Available on ORBi :
since 18 April 2018

Statistics


Number of views
549 (78 by ULiège)
Number of downloads
869 (38 by ULiège)

Scopus citations®
 
30
Scopus citations®
without self-citations
20
OpenCitations
 
25

Bibliography


Similar publications



Contact ORBi