SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
VCC Analytics Research Group
Visual Computing Center (VCC)
Date
2018-12-18Preprint Posting Date
2018-04-12Online Publication Date
2018-12-18Print Publication Date
2018-06Permanent link to this record
http://hdl.handle.net/10754/627570
Metadata
Show full item recordAbstract
In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances d ranging from 5 to 60 seconds. Our dataset and models are available at https://silviogiancola.github.io/SoccerNet.Citation
Giancola S, Amine M, Dghaily T, Ghanem B (2018) SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Available: http://dx.doi.org/10.1109/CVPRW.2018.00223.Sponsors
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.Conference/Event name
31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018arXiv
1804.04527Additional Links
https://ieeexplore.ieee.org/document/8575386ae974a485f413a2113503eed53cd6c53
10.1109/CVPRW.2018.00223