  Soccer videos can serve as a perfect research object for video understanding
because soccer games are played under well-defined rules while complex and
intriguing enough for researchers to study. In this paper, we propose a new
soccer video database named SoccerDB, comprising 171,191 video segments from
346 high-quality soccer games. The database contains 702,096 bounding boxes,
37,709 essential event labels with time boundary and 17,115 highlight
annotations for object detection, action recognition, temporal action
localization, and highlight detection tasks. To our knowledge, it is the
largest database for comprehensive sports video understanding on various
aspects. We further survey a collection of strong baselines on SoccerDB, which
have demonstrated state-of-the-art performances on independent tasks. Our
evaluation suggests that we can benefit significantly when jointly considering
the inner correlations among those tasks. We believe the release of SoccerDB
will tremendously advance researches around comprehensive video understanding.
{\itshape Our dataset and code published on
https://github.com/newsdata/SoccerDB.}
