Automatically extracting highlights for TV Baseball programs

By Yong Rui, Anoop Gupta, A. Acero. ACM Multimedia, 2000

505 citations recorded by the source corpus.

In today's fast-paced world, while the number of channels of television programming available is increasing rapidly, the time available to watch them remains the same or is decreasing. Users desire the capability to watch the programs time-shifted (on-demand) and/or to watch just the highlights to save time. In this paper we explore how to provide for the latter capability, that is the ability to extract highlights automatically, so that viewing time can be reduced. We focus on the sport of baseball as our initial target—it is a very popular sport, the whole game is quite long, and the exciting portions are few. We focus on detecting highlights using audio-track features alone without relying on expensive-to-compute video-track features. We use a combination of generic sports features and baseball-specific features to obtain our results, but believe that may other sports offer the same opportunity and that the techniques presented here will apply to those sports. We present details on relative performance of various learning algorithms, and a probabilistic framework for combining multiple sources of information. We present results comparing output of our algorithms against human-selected highlights for a diverse collection of baseball games with very encouraging results.

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5138a7d65d6875191572291f3458b4de149b4d2a

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Citations in the corpus

Title Generation for User Generated Videos (2016, background): “Most early highlight detection works focus on broadcasting sport videos [28,29,30,31,32,33,34,35].”
Semi-supervised adapted HMMs for unusual event detection (2005, background): “In particular, some recent work has attempted to recognize highlights in videos, e.g., sports [15, 7, 18].”
Ranking Domain-Specific Highlights by Analyzing Edited Videos (2014, background): “, [27,20,16,26,25,10,4,23], most methods require large amounts of human-crafted training data.”
Multimodal Video Indexing: A Review of the State-of-the-art (2004, methodology): “An exception to this common approach is [71], which presents an algorithm that identifies highlights in baseball video by analyzing the auditory modality only.”
Unsupervised Semantic Parsing of Video Collections (2015, methodology): “Summarizing videos is particularly important for long sequences like ego-centric videos and news reports [35, 38, 50]; however, these methods mostly rely on characteristics of the application and do not generalize.”
Video summarisation: A conceptual framework and survey of the state of the art (2008, methodology): “[63] analyse the speech track in baseball videos and apply a model derived from human speech processing theory (in the form of phoneme-level feature extraction) to abstract exciting segments and events such as baseball hits from baseball games, while Otsuka et al.”
Category-Specific Video Summarization (2014, unknown): “For example, soccer games [13,14] or feature films [15] have standard phases that can be manually identified.”