Name
Title | ||
|---|---|---|
A Generic Framework for Semantic Sports Video Analysis Using Dynamic Bayesian Networks |
Abstract | ||
|---|---|---|
Automatic detection of semantic events in sport videos is a challenging task. In this paper, we propose a multimodal multilayer statistical inference framework for semantic sports video analysis using Dynamic Bayesian Networks (DBNs). Based on this framework, three instances including factorial hierarchical hidden Markov model (FHHMM), coupled hierarchical hidden Markov model (CHHMM), and product hierarchical hidden Markov model (PHHMM), are constructed and compared. Play-break detection in soccer videos is used as a testbed with hierarchical hidden Markov model (HHMM) as a baseline. Experimental results indicate the superior capability of the PHHMM, because it not only effectively models dynamic interactions between different modalities, but also sufficiently utilizes context constraints in multilayer structures. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/MMMC.2005.9 | MMM |
Keywords | Field | DocType |
semantic sports video analysis,event detection,semantic event,sports video analysis,challenging task,hierarchical hidden markov model,multimodal multilayer statistical inference,statistical modeling,automatic detection,dynamic bayesian networks,different modality,semantic sport,play-break detection,multilayer structure,generic framework,statistical model,dynamic bayesian network,statistical inference | Variable-order Bayesian network,Pattern recognition,Computer science,Hierarchical hidden Markov model,Testbed,Factorial,Statistical model,Statistical inference,Artificial intelligence,Hidden Markov model,Machine learning,Dynamic Bayesian network | Conference |
ISSN | ISBN | Citations |
1550-5502 | 0-7695-2164-9 | 21 |
PageRank | References | Authors |
0.82 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Wang | 1 | 125 | 45.09 |
| Yu-Fei Ma | 2 | 1166 | 63.05 |
| Hong-Jiang ZHANG | 3 | 17378 | 1393.22 |
| Jintao Li | 4 | 1488 | 111.30 |