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The automatic detection of events in sport videos has im-portant applicationsfor data analytics, as well as for broadcasting andmedia companies. This paperpresents a comprehensive approach for de-tecting a wide range of complex eventsin soccer videos starting frompositional data. The event detector is designedas a two-tier system thatdetectsatomicandcomplex events. Atomic events aredetected basedon temporal and logical combinations of the detected objects,their rel-ative distances, as well as spatio-temporal features such as velocityandacceleration. Complex events are defined as temporal and logicalcom-binations of atomic and complex events, and are expressed by meansof adeclarative Interval Temporal Logic (ITL). The effectiveness of theproposedapproach is demonstrated over 16 different events, includingcomplex situationssuch as tackles and filtering passes. By formalizingevents based on principledITL, it is possible to easily perform reason-ing tasks, such as understandingwhich passes or crosses result in a goalbeing scored. To counterbalance thelack of suitable, annotated publicdatasets, we built on an open source soccersimulation engine to re-lease the synthetic SoccER (Soccer Event Recognition)dataset, whichincludes complete positional data and annotations for more than1.6 mil-lion atomic events and 9,000 complex events. The dataset and codeareavailable at https://gitlab.com/grains2/slicing-and-dicing-soccer

Slicing and dicing soccer: automatic detection of complex events from spatio-temporal data