Abstract

Video events detection or recognition is one of important tasks in semantic understanding of video content. Sports game video should be considered as a rule-based sequential signal. Therefore, it is reasonable to model sports events using hidden Markov models. In this paper, we present a generic, scalable and multilayer framework based on HMMs, called SG-HMMs (sports game HMMs), for sports game event detection. At the bottom layer of this framework, event HMMs output basic hypotheses based on low-level features. The upper layers are composed of composition HMMs, which add constraints on those hypotheses of the lower layer. Instead of isolated event recognition, the hypotheses at different layers are optimized in a bottom-up manner and the optimal semantics are determined by top-down process. The experimental results on basketball and volleyball videos have demonstrated the effectiveness of the proposed framework for sports game analysis.

Department(s)

Chemistry

Keywords and Phrases

Basketball Video; Feature Extraction; Hidden Markov Models; Image Recognition; Low-Level Features; Semantic Analysis; Sport; Sports Game HMM; Sports Game Event Detection; Top-Down Process; Video Events Detection; Video Recognition; Video Signal Processing; Volleyball Video

International Standard Serial Number (ISSN)

1522-4880

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Included in

Chemistry Commons

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