Group activity detection (GAD) is the task of identifying members of each
group and classifying the activity of the group at the same time in a video.
While GAD has been studied recently, there is still much room for improvement
in both dataset and methodology due to their limited capability to address
practical GAD scenarios. To resolve these issues, we first present a new
dataset, dubbed Caf\'e. Unlike existing datasets, Caf\'e is constructed
primarily for GAD and presents more practical evaluation scenarios and metrics,
as well as being large-scale and providing rich annotations. Along with the
dataset, we propose a new GAD model that deals with an unknown number of groups
and latent group members efficiently and effectively. We evaluated our model on
three datasets including Caf\'e, where it outperformed previous work in terms
of both accuracy and inference speed. Both our dataset and code base will be
open to the public to promote future research on GAD.

Group activity detection addresses the identification and classification of group members' activities in videos. This paper introduces a new dataset called Café, designed specifically for GAD, along with a novel GAD model that efficiently handles unknown group sizes and latent group members, achieving superior performance in accuracy and inference speed.

朝着更实践的群体活动检测：新的基准和模型

Towards More Practical Group Activity Detection: A New Benchmark and  Model

Group activity detection in soccer can be done by using either video data or
player and ball trajectory data. In current soccer activity datasets,
activities are labelled as atomic events without a duration. Given that the
state-of-the-art activity detection methods are not well-defined for atomic
actions, these methods cannot be used. In this work, we evaluated the
effectiveness of activity recognition models for detecting such events, by
using an intuitive non-maximum suppression process and evaluation metrics. We
also considered the problem of explicitly modeling interactions between players
and ball. For this, we propose self-attention models to learn and extract
relevant information from a group of soccer players for activity detection from
both trajectory and video data. We conducted an extensive study on the use of
visual features and trajectory data for group activity detection in sports
using a large scale soccer dataset provided by Sportlogiq. Our results show
that most events can be detected using either vision or trajectory-based
approaches with a temporal resolution of less than 0.5 seconds, and that each
approach has unique challenges.

本研究使用轨迹和视觉数据来检测足球比赛中的群体活动，并提出了自我注意力模型来显式地建模球员与球之间的交互作用。结果表明，基于视觉或轨迹的方法都能够在 0.5 秒内识别出大部分事件，并且每种方法都有其独特的挑战。