The SoccerNet 2022 challenges were the second annual video understanding
challenges organized by the SoccerNet team. In 2022, the challenges were
composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving
action timestamps in long untrimmed videos, (2) replay grounding, focusing on
retrieving the live moment of an action shown in a replay, (3) pitch
localization, focusing on detecting line and goal part elements, (4) camera
calibration, dedicated to retrieving the intrinsic and extrinsic camera
parameters, (5) player re-identification, focusing on retrieving the same
players across multiple views, and (6) multiple object tracking, focusing on
tracking players and the ball through unedited video streams. Compared to last
year's challenges, tasks (1-2) had their evaluation metrics redefined to
consider tighter temporal accuracies, and tasks (3-6) were novel, including
their underlying data and annotations. More information on the tasks,
challenges and leaderboards are available on this https URL
Baselines and development kits are available on this https URL

SoccerNet 2022 挑战赛是由 SoccerNet 团队组织的第二次年度视频理解挑战赛，包括 6 个基于视觉的任务，涵盖了动作检测、球员识别和跟踪等领域。

SoccerNet 2022 挑战赛结果

SoccerNet 2022 Challenges Results

In video understanding, action spotting consists in temporally localizing
human-induced events annotated with single timestamps. In this paper, we
propose a novel loss function that specifically considers the temporal context
naturally present around each action, rather than focusing on the single
annotated frame to spot. We benchmark our loss on a large dataset of soccer
videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We
show the generalization capability of our loss for generic activity proposals
and detection on ActivityNet, by spotting the beginning and the end of each
activity. Furthermore, we provide an extended ablation study and display
challenging cases for action spotting in soccer videos. Finally, we
qualitatively illustrate how our loss induces a precise temporal understanding
of actions and show how such semantic knowledge can be used for automatic
highlights generation.

本文提出了一种新的损失函数，它特别考虑了每个动作周围的时间上下文，而不是只关注于单个时间戳，然后在 SoccerNet 上测试表现，取得了 12.8％的改进，同时展示了该方法用于通用活动提议和检测的泛化能力，进而探讨了足球视频中动作定位的挑战和损失函数如何用于自动生成亮点。

足球视频中动作检测的上下文感知损失函数

A Context-Aware Loss Function for Action Spotting in Soccer Videos

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。