Combining multiple tracking modalities for vehicle tracking at traffic intersections

Harini Veeraraghavan, Nikolaos P Papanikolopoulos

Research output: Contribution to journalConference article

21 Scopus citations

Abstract

This paper presents a camera-based system for tracking vehicles at outdoor scenes such as traffic intersections. Two different computer vision modalities, namely, the connected regions obtained through region segmentation and color analysis, obtained through a mean-shift tracking procedure are combined sequentially using an Extended Kalman filter to provide the position of each target. Data association ambiguities arising in blob tracking are handled by using oriented bounding boxes and a Joint Probabilistic Data Association filter. We show that the above tracking formulation can provide reasonable tracking despite the stop-and-go motion of vehicles and clutter in traffic intersections.

Original languageEnglish (US)
Pages (from-to)2303-2308
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2004
Issue number3
StatePublished - Jul 5 2004
EventProceedings- 2004 IEEE International Conference on Robotics and Automation - New Orleans, LA, United States
Duration: Apr 26 2004May 1 2004

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Keywords

  • Blob tracking
  • Joint Probabilistic Data Association filter
  • Mean Shift tracking
  • Stop-and-go traffic

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