Real-time background estimation of traffic imagery using group-based histogram

Kai-Tai Song*, Jen Chao Tai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

This paper presents a novel background estimation method for a vision-based traffic monitoring system using a single Gaussian scheme. An algorithm of group-based histogram (GBH) is proposed to build the background Gaussian model of each pixel from traffic image sequences. This algorithm features improved robustness against transient stop of foreground objects and sensing noise. Furthermore, the method features low computational load, thus meets the real-time requirements in many practical applications. The proposed method has been applied to a vision-based traffic parameter estimation system to segment moving vehicles from image sequences. Given degraded compressive traffic images from on-line internet cameras, the image processing system successfully detect various vehicles in the traffic imagery. Practical experimental results demonstrate that traffic flow can be measured in real time with satisfactory accuracy.

Original languageEnglish
Pages (from-to)411-423
Number of pages13
JournalJournal of Information Science and Engineering
Volume24
Issue number2
DOIs
StatePublished - 1 Mar 2008

Keywords

  • Background segmentation
  • Group-based histogram
  • Image processing
  • Traffic monitoring
  • Traffic parameters

Fingerprint Dive into the research topics of 'Real-time background estimation of traffic imagery using group-based histogram'. Together they form a unique fingerprint.

Cite this