  The automatic detection of events in sport videos has im-portant applications
for data analytics, as well as for broadcasting andmedia companies. This paper
presents a comprehensive approach for de-tecting a wide range of complex events
in soccer videos starting frompositional data. The event detector is designed
as a two-tier system thatdetectsatomicandcomplex events. Atomic events are
detected basedon temporal and logical combinations of the detected objects,
their rel-ative distances, as well as spatio-temporal features such as velocity
andacceleration. Complex events are defined as temporal and logical
com-binations of atomic and complex events, and are expressed by meansof a
declarative Interval Temporal Logic (ITL). The effectiveness of theproposed
approach is demonstrated over 16 different events, includingcomplex situations
such as tackles and filtering passes. By formalizingevents based on principled
ITL, it is possible to easily perform reason-ing tasks, such as understanding
which passes or crosses result in a goalbeing scored. To counterbalance the
lack of suitable, annotated publicdatasets, we built on an open source soccer
simulation engine to re-lease the synthetic SoccER (Soccer Event Recognition)
dataset, whichincludes complete positional data and annotations for more than
1.6 mil-lion atomic events and 9,000 complex events. The dataset and code
areavailable at https://gitlab.com/grains2/slicing-and-dicing-soccer
