Soccerstar-v1-pc_uq.7z Access

: It addressed the lack of large-scale, publicly available datasets for automated soccer video understanding, enabling the training of deep learning models for sports analytics. Evolutions of the Dataset

: The paper proposes using recent developments in action recognition and detection to provide baselines, reaching a mean Average Precision (mAP) of 67.8% for classifying 1-minute temporal segments. SoccerStar-v1-pc_UQ.7z

: Focuses on three primary event types: Goals , Yellow/Red Cards , and Substitutions . : It addressed the lack of large-scale, publicly

: 500 complete soccer games from major European leagues (2014–2017), totaling 764 hours of video. : 500 complete soccer games from major European

The dataset was introduced by Silvio Giancola et al. at the CVPR 2018 Workshop on Computer Vision in Sports. It was designed to solve the problem of —temporally localizing sparse events like goals or cards within long video broadcasts.

The file likely contains the first version of the SoccerNet dataset (often referred to as SN-v1 ), which is the foundation for the landmark paper SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos . The "Deep" Paper: SoccerNet (SN-v1)

A Scalable Dataset for Action Spotting in Soccer Videos - arXiv