NDLI: Extract highlights from baseball game video with hidden Markov models

Content Provider	IEEE Xplore Digital Library
Author	Peng Chang Mei Han Yihong Gong
Copyright Year	2002
Description	Author affiliation: Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Peng Chang)
Abstract	We describe a statistical method to detect highlights in a baseball game video. The input video is first segmented into scene shots, within which the camera motion is continuous. Our approach is based on the observations that (1) most highlights in baseball games are composed of certain types of scene shots and (2) those scene shots exhibit special transition context in time. To exploit those two observations, we first build statistical models for each type of scene shots with products of histograms, and then for each type of highlight a hidden Markov model is learned to represent the context of transition in the time domain. A probabilistic model can be obtained by combining the two, which is used for highlight detection and classification. Satisfactory results have been achieved on initial experimental results.
Sponsorship	IEEE Signal Process. Soc
File Size	265048
File Format	PDF
ISBN	0780376226
ISSN	15224880
DOI	10.1109/ICIP.2002.1038097
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Games Layout Cameras Multimedia communication Broadcasting Streaming media Statistical analysis Robot vision systems Context modeling
Content Type	Text
Resource Type	Article

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