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Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 10 | Page No.: 1381-1390
DOI: 10.3923/itj.2012.1381.1390
 
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A Framework Based on Multi-models and Multi-features for Sports Video Semantic Analysis

Jiaqi Fu, Hongping Hu, Richao Chen and Heng Ren

Abstract:
The proliferation of video posed a challenging problem for the automatic analysis, interpretation and indexing of video data. Among them, sports video analysis has attracted the most attention because of the appeal of sports to large audience. This study presented an effective sports video semantic analysis algorithm based on the fusion and interaction of multi-models and multi-features. By utilizing the semantic color ratio, the video shot was classified into global shot, in-field shot and out-of-field shot which facilitated the HMM-based classification. For shot corresponding to a specific scene, by introducing image registration, the artifacts of noise and camera movement were reduced and accurate local motion features were obtained. Then, Hidden Markov Models (HMMs) were exploited to associate every video shot with a particular semantic class. Experimental results on Football and Tennis sequence showed that the proposed approach can achieve a relatively high ratio of correct semantic recognition.
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How to cite this article:

Jiaqi Fu, Hongping Hu, Richao Chen and Heng Ren, 2012. A Framework Based on Multi-models and Multi-features for Sports Video Semantic Analysis. Information Technology Journal, 11: 1381-1390.

DOI: 10.3923/itj.2012.1381.1390

URL: https://scialert.net/abstract/?doi=itj.2012.1381.1390

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