Multimodal soccer highlight identification using a sparse subset of frames integrating long-term sliding windows

Abstract

The massive growth of audiences eager for sport content has substantially increased workers’ demand in this profitable segment. Highlight identification is vital for summarizing football matches. Decision support tools can significantly reduce the number of company employees required to tackle such a task, widely benefiting workforce resource allocation. This paper discusses the development of an automatic football highlight detector. The proposed system exploits discriminative low-level audio and video features extracted from a compact set of irregularly time–spaced frames that integrate a long-term sliding window. A new mixed wrapper-probabilistic algorithm leverages a cost-effective selection of the most significant frames submitted to a robust multi-frame consensus classification scheme. By considering a comprehensive database integrating 30 full matches, the proposed approach achieves a highlight identification rate of 100% (including all annotated goals), conjugated with a match-time compression rate of about 94%, when employing a Random Forest classifier.