  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.
