IBM TRECVID'08 high-level feature detection
Apostol Natsev, Wei Jiang, et al.
TRECVID 2008
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 use it to create a real-world system for the editorial aid of golf highlight reels. Our method fuses information from the players' reactions (action recognition such as high-fives and fist pumps), spectators (crowd cheering), and commentator (tone of the voice and word analysis) 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, 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, successfully extracting highlights from live video streams over four consecutive days.
Apostol Natsev, Wei Jiang, et al.
TRECVID 2008
John Smith, Dhiraj Joshi, et al.
MM 2017
Khoi Nguyen Mac, Dhiraj Joshi, et al.
ICCV 2019
Michele Merler, Khoi Nguyen Mac, et al.
IEEE TMM