Spatiotemporal vehicle tracking: The use of unsupervised learning-based segmentation and object tracking

Shu Ching Chen, Mei Ung Shyu, Srinivas Peeta, Chengcui Zhang

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

An adaptive background-learning and subtraction method is proposed and applied to two real-life traffic video sequences to obtain more accurate spatiotemporal information on the vehicle objects. When paired with the image segmentation, the method is robust under many conditions. A key advantage of the method is that it is fully automated and unsupervised; it performs the generation of background images using a self-triggered mechanism.

Original languageEnglish (US)
Pages (from-to)50-58
Number of pages9
JournalIEEE Robotics and Automation Magazine
Volume12
Issue number1
DOIs
StatePublished - Mar 2005

Keywords

  • Intelligent transportation systems
  • Robotic vision
  • Segmentation
  • Vehicle tracking
  • Video analysis

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Spatiotemporal vehicle tracking: The use of unsupervised learning-based segmentation and object tracking'. Together they form a unique fingerprint.

  • Cite this