  The automatic detection of events in complex sports games like soccer and
handball using positional or video data is of large interest in research and
industry. One requirement is a fundamental understanding of underlying
concepts, i.e., events that occur on the pitch. Previous work often deals only
with so-called low-level events based on well-defined rules such as free kicks,
free throws, or goals. High-level events, such as passes, are less frequently
approached due to a lack of consistent definitions. This introduces a level of
ambiguity that necessities careful validation when regarding event annotations.
Yet, this validation step is usually neglected as the majority of studies adopt
annotations from commercial providers on private datasets of unknown quality
and focuses on soccer only. To address these issues, we present (1) a universal
taxonomy that covers a wide range of low and high-level events for invasion
games and is exemplarily refined to soccer and handball, and (2) release two
multi-modal datasets comprising video and positional data with gold-standard
annotations to foster research in fine-grained and ball-centered event
spotting. Experiments on human performance demonstrate the robustness of the
proposed taxonomy, and that disagreements and ambiguities in the annotation
increase with the complexity of the event. An I3D model for video
classification is adopted for event spotting and reveals the potential for
benchmarking. Datasets are available at: https://github.com/mm4spa/eigd

Computer Vision and Pattern Recognition