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Extract Soccer Video Highlights with Modified CRF

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Abstract:

Soccer video analysis is attracting much attention. In this paper, we propose a modified conditional random field (CRF) model to extract the highlights of an entire soccer video. Highlight extraction in soccer video is essentially a kind of timing annotation problems, so the commonly used CRF model is adopted to solve this problem in this paper. Meanwhile, Boolean function is normally used as feature function in the CRF model, which will result in a hard association between observed variables and highlight variables. By introducing Bayesian network to model the observed variables and replacing the original feature function with posterior probability calculated with Bayesian network, hard association is transformed into soft association, which makes the model more close to the actual situation. Experimental results show that the proposed algorithm has achieved good results.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

3426-3429

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.3426

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Cite this paper

Online since:

May 2014

Authors:

Nai Gu Huang, Yang Yi*, Yu Lin Wang, Peng Fei Zhu

Keywords:

Conditional Random Field, Feature Function, Highlight Extraction, Posterior Probability

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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