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Part of book or chapter of book
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https://doi.org/10.1007/115780...
Part of book or chapter of book . 2005 . Peer-reviewed
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Automatic Annotation of Sport Video Content

Authors: Marco Bertini 0001; Alberto Del Bimbo; Walter Nunziati;

Automatic Annotation of Sport Video Content

Abstract

Automatic semantic annotation of video streams allows to extract significant clips for archiving and retrieval of video content. In this paper, we present a system that performs automatic annotation of soccer videos, detecting principal highlights, and recognizing identity of players. Highlight detection is carried out by means of finite state machines that encode domain knowledge, while player identification is based on face detection, and on the analysis of contextual information such as jersey’s numbers and superimposed text captions. Results obtained on actual soccer videos shows overall highlight detection rates of about 90%. Lower, but still promising, accuracy is achieved on the very difficult player identification task.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
Green
bronze