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
$\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at
this https URL

该研究介绍了 SoccerNet，这是一个针对足球视频中动作定位的基准数据集，涵盖 2014 年至 2017 年三个赛季的六个欧洲主要联赛中的 500 场比赛，其中自动分析了 6,637 个事件的时间注释。作者们利用了通用动作识别和检测的最新进展，提供了针对足球事件检测的强大基线。