Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems

Shu Ching Chen, Mei-Ling Shyu, Srinivas Peeta, Chengcui Zhang

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

One key technology of intelligent transportation systems is the use of advanced sensor systems for on-line surveillance to gather detailed information on traffic conditions. Traffic video analysis can provide a wide range of useful information to traffic planners. In this context, the object-level indexing of video data can enable vehicle classification, traffic flow analysis, incident detection, and analysis at intersections, vehicle tracking for traffic operations, and update of design warrants. In this paper, a learning-based automatic framework is proposed to support the multimedia data indexing and querying of spatio-temporal relationships of vehicle objects in a traffic video sequence. The spatio-temporal relationships of vehicle objects are captured via the proposed unsupervised image/video segmentation method and object tracking algorithm, and modeled using a multimedia augmented transition network model and multimedia input strings. An efficient and effective background learning and subtraction technique is employed to eliminate the complex background details in the traffic video frames. It substantially enhances the efficiency of the segmentation process and the accuracy of the segmentation results to enable more accurate video indexing and annotation. The paper uses four real-life traffic video sequences from several road intersections under different weather conditions in the study experiments. The results show that the proposed framework is effective in automating data collection and access for complex traffic situations.

Original languageEnglish
Pages (from-to)154-167
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume4
Issue number3
DOIs
StatePublished - Sep 1 2003

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Telecommunication traffic
Sensors
Experiments

Keywords

  • Advanced traffic management systems (ATMS)
  • Advanced traveler information systems (ATIS)
  • Background learning and background subtraction
  • Intelligent transportation systems (ITS)
  • Multimedia database indexing
  • Segmentation
  • Vehicle tracking
  • Video analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Electrical and Electronic Engineering

Cite this

Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems. / Chen, Shu Ching; Shyu, Mei-Ling; Peeta, Srinivas; Zhang, Chengcui.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 4, No. 3, 01.09.2003, p. 154-167.

Research output: Contribution to journalArticle

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