The production of sports highlight packages summarizing a game’s most exciting moments is an essential task for broadcast media. Yet, it requires labor intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis) and game analytics to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player’s name and the hole number, or analysts input allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament (2017 Masters) and two major international tennis tournaments (2017 Wimbledon and US Open), successfully extracting highlights through the course of the sporting events. 54% of the clips selected by our system overlapped with the official highlights reels for the 2017 Masters. Furthermore, user studies showed that people found that 90% of the non-overlapping ones where of the same quality of the official clips for 2017 Masters, while the automatic selection of clips for highlights of 2017 Wimbledon and 2017 US Open agreed with human preferences 80% and 84.2% of the time, respectively.
Michele Merler, Dhiraj Joshi, Quoc-Bao Nguyen, Stephen Hammer, John Kent, Jinjun Xiong, Minh N. Do, John R Smith, Rogerio S Feris. Automatic Curation of Sports Highlights using Multimodal Excitement Features. IEEE Transactions on Multimedia (TMM) 2018. PDF (preprint) BibTeX
