SFERAnet: automatic generation of football highlights

We present a methodology for automatic generation of football match “highlights”, relying on the commentator voices and leveraging two multimodal NNs. The fist model (M1) classifies sequences and provides a representation of such sequences to be elaborated by the second model. M2 exploits M1 to decode unbound streams of information, generating the final set of scenes to put into the match summary. Raw audio, along with transcriptions generated by an ASR, extracted from 369 football matches provided the source for feature extraction. We employed such features to train M1 and M2; for M1, the feature streams were split in sequences at (nearly) sentence granularity, while for M2 the entire streams were employed. The final results were promising, especially if adopted in a semi-automatic, real-world video pipeline.