DocumentCode :
2306895
Title :
A HMM based semantic analysis framework for sports game event detection
Author :
Xu, Gu ; Ma, Yu-Fei ; Zhang, Hong-Jiang ; Yang, Shiqiang
Author_Institution :
Dept. of Comput. Sci., Tsinghua Univ., Beijing, China
Volume :
1
fYear :
2003
fDate :
14-17 Sept. 2003
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.
Keywords :
feature extraction; hidden Markov models; image recognition; sport; video signal processing; basketball video; hidden Markov models; low-level features; semantic analysis; sports game HMM; sports game event detection; top-down process; video events detection; video recognition; volleyball video; Asia; Cameras; Clustering algorithms; Computer science; Data mining; Event detection; Games; Hidden Markov models; Information retrieval; Speech;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-7750-8
Type :
conf
DOI :
10.1109/ICIP.2003.1246889
Filename :
1246889
Link To Document :
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