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.
在当今快节奏的世界中,尽管可用的电视节目频道数量正在迅速增加,但观看这些节目的时间却保持不变或正在减少。用户希望能够观看时移节目(按需)和/或仅观看精彩片段以节省时间。本文探讨了如何提供后一种功能,即自动提取高光的功能,从而可以减少观看时间。 P>
我们将棒球运动作为最初的目标-这是一项非常受欢迎的运动,整个比赛都相当长,而令人兴奋的部分却很少。我们专注于仅使用音频轨道功能来检测亮点,而不依赖于昂贵的计算视频轨道功能。我们结合了通用运动功能和特定于棒球的功能来获得结果,但相信其他运动可能会提供相同的机会,并且此处介绍的技术将适用于这些运动。我们将介绍各种学习算法的相对性能以及组合多种信息源的概率框架的详细信息。我们给出了将我们的算法输出与人类选择的亮点进行比较的结果,这些亮点用于各种棒球比赛,结果令人鼓舞。 P>
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机译:自动提取电视棒球节目的精彩片段
机译:棒球计划:自动问题 - aNsWERERnVOLUmE I