DocumentCode :
1199803
Title :
An HMM-based framework for video semantic analysis
Author :
Xu, Gu ; Ma, Yu-Fei ; Zhang, Hong-Jiang ; Yang, Shi-Qiang
Author_Institution :
Microsoft Res. Asia, Beijing, China
Volume :
15
Issue :
11
fYear :
2005
Firstpage :
1422
Lastpage :
1433
Abstract :
Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis.
Keywords :
hidden Markov models; image classification; image motion analysis; image representation; image sequences; indexing; sport; video coding; HMM-based framework; basketball event detection; connectors; detectors; hidden Markov model; motion representation scheme; recognition; soccer shot classification; sports video; training process; vector source; video indexing; video semantic analysis; video structuring; volleyball sequence analysis; Algorithm design and analysis; Books; Connectors; Detectors; Event detection; Hidden Markov models; Humans; Indexing; Robustness; Speech recognition; Event detection; hidden Markov models (HMMs); sports videos; video semantic analysis;
fLanguage :
English
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1051-8215
Type :
jour
DOI :
10.1109/TCSVT.2005.856903
Filename :
1522268
Link To Document :
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