A Deep Architecture for Multimodal Summarization of Soccer Games

The massive growth of sports videos, specially in soccer, has resulted in a need for the automatic generation of summaries, where the objective is not only to show the most important actions of the match but also to elicit as much emotion as the ones bring upon by human editors. State-of-the-art methods on video summarization mostly rely on video processing, however this is not an optimal approach for long videos such as soccer matches. In this paper we propose a multimodal approach to automatically generate summaries of soccer match videos that consider both event and audio features. The event features get a shorter and better representation of the match, and the audio helps detect the excitement generated by the game. Our method consists of three consecutive stages: Proposals, Summarization and Content Refinement. The first one generates summary proposals, using Multiple Instance Learning to deal with the similarity between the events inside the summary and the rest of the match. The Summarization stage uses event and audio features as input of a hierarchical Recurrent Neural Network to decide which proposals should indeed be in the summary. And the last stage, takes advantage of the visual content to create the final summary. The results show that our approach outperforms by a large margin not only the video processing methods but also methods that use event and audio features.