Automatically Extracting Highlights for TV Baseball Programs

Y. Rui, A. Gupta, and Alex Acero


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 (ondemand)

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 audiotrack

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 many 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.


Publication typeArticle
Published inACM Multimedia, pp. 105-115
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