A multimedia data mining framework: Mining information from traffic video sequences

Shu Ching Chen, Mei-Ling Shyu, Chengcui Zhang, Jeff Strickrott

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The analysis and mining of traffic video sequences to discover important but previously unknown knowledge such as vehicle identification, traffic flow, queue detection, incident detection, and the spatio-temporal relations of the vehicles at intersections, provide an economic approach for daily traffic monitoring operations. To meet such demands, a multimedia data mining framework is proposed in this paper. The proposed multimedia data mining framework analyzes the traffic video sequences using background subtraction, image/video segmentation, vehicle tracking, and modeling with the multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over traffic intersections. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-à-vis traffic operations. Three real-life traffic video sequences obtained from different sources and with different weather conditions are used to illustrate the effectiveness and robustness of the proposed multimedia data mining framework by demonstrating how the proposed framework can be applied to traffic applications to answer the spatio-temporal queries.

Original languageEnglish
Pages (from-to)61-77
Number of pages17
JournalJournal of Intelligent Information Systems
Volume19
Issue number1
DOIs
StatePublished - Jul 1 2002

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Data mining
Monitoring
Automation
Economics

Keywords

  • Multimedia augmented transition network (MATN)
  • Multimedia data mining
  • Spatio-temporal relationships
  • Vehicle tracking

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

A multimedia data mining framework : Mining information from traffic video sequences. / Chen, Shu Ching; Shyu, Mei-Ling; Zhang, Chengcui; Strickrott, Jeff.

In: Journal of Intelligent Information Systems, Vol. 19, No. 1, 01.07.2002, p. 61-77.

Research output: Contribution to journalArticle

Chen, Shu Ching ; Shyu, Mei-Ling ; Zhang, Chengcui ; Strickrott, Jeff. / A multimedia data mining framework : Mining information from traffic video sequences. In: Journal of Intelligent Information Systems. 2002 ; Vol. 19, No. 1. pp. 61-77.
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