DocumentCode
2714428
Title
A unified framework for event summarization and rare event detection
Author
Kwon, Junseok ; Lee, Kyoung Mu
Author_Institution
Dept. of EECS, Seoul Nat. Univ., Seoul, South Korea
fYear
2012
fDate
16-21 June 2012
Firstpage
1266
Lastpage
1273
Abstract
In this paper, we have proposed an unified framework for event summarization and rare event detection and presented the graph-structure learning and editing method to solve these problems efficiently. The experimental results demonstrated that the proposed method outperformed conventional algorithms in complex and crowded public scenes by exploiting and utilizing causality, frequency, and significance of relations of events.
Keywords
graph theory; learning (artificial intelligence); video signal processing; complex public scenes; crowded public scenes; editing method; event summarization; graph-structure learning; rare event detection; unified framework; Data mining; Density functional theory; Event detection; Frequency measurement; Hidden Markov models; Markov processes; Proposals;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location
Providence, RI
ISSN
1063-6919
Print_ISBN
978-1-4673-1226-4
Electronic_ISBN
1063-6919
Type
conf
DOI
10.1109/CVPR.2012.6247810
Filename
6247810
Link To Document