Semantic event detection via multimodal data mining

Author

Chen, Min ; Chen, Shu-Ching ; Shyu, Mei-Ling ; Wickramaratna, Kasun

Author_Institution

Sch. of Comput. & Inc. Sci., Florida Int. Univ., Miami, FL

Volume

23

Issue

2

fYear

2006

fDate

3/1/2006 12:00:00 AM

Firstpage

38

Lastpage

46

Abstract

This paper presents a novel framework for video event detection. The core of the framework is an advanced temporal analysis and multimodal data mining method that consists of three major components: low-level feature extraction, temporal pattern analysis, and multimodal data mining. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The framework is presented with its application to the detection of the soccer goal events over a large collection of soccer video data with various production styles

Keywords

data mining; feature extraction; video signal processing; event patterns; little human interference; low-level feature extraction; multimodal data mining; semantic event detection; soccer goal events; soccer video data; temporal analysis; temporal pattern analysis; video event detection; Data analysis; Data mining; Decision making; Event detection; Feature extraction; Hidden Markov models; Humans; Interference; Pattern analysis; Production;

fLanguage

English

Journal_Title

Signal Processing Magazine, IEEE

Publisher

ieee

ISSN

1053-5888

Type

jour

DOI

10.1109/MSP.2006.1621447

Filename

1621447