  Soccer analytics is attracting increasing interest in academia and industry,
thanks to the availability of data that describe all the spatio-temporal events
that occur in each match. These events (e.g., passes, shots, fouls) are
collected by human operators manually, constituting a considerable cost for
data providers in terms of time and economic resources. In this paper, we
describe PassNet, a method to recognize the most frequent events in soccer,
i.e., passes, from video streams. Our model combines a set of artificial neural
networks that perform feature extraction from video streams, object detection
to identify the positions of the ball and the players, and classification of
frame sequences as passes or not passes. We test PassNet on different
scenarios, depending on the similarity of conditions to the match used for
training. Our results show good classification results and significant
improvement in the accuracy of pass detection with respect to baseline
classifiers, even when the match's video conditions of the test and training
sets are considerably different. PassNet is the first step towards an automated
event annotation system that may break the time and the costs for event
annotation, enabling data collections for minor and non-professional divisions,
youth leagues and, in general, competitions whose matches are not currently
annotated by data providers.
