Combining multiple tracking modalities for vehicle tracking at traffic intersections

Harini Veeraraghavan, Nikolaos P Papanikolopoulos

Research output: Contribution to journalArticle

21 Citations (Scopus)

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

Fingerprint

Telecommunication traffic
Extended Kalman filters
Computer vision
Cameras
Color

Keywords

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

Cite this

Combining multiple tracking modalities for vehicle tracking at traffic intersections. / Veeraraghavan, Harini; Papanikolopoulos, Nikolaos P.

In: Proceedings - IEEE International Conference on Robotics and Automation, Vol. 2004, No. 3, 05.07.2004, p. 2303-2308.

Research output: Contribution to journalArticle

@article{e3a36966e7f249349194baa811b62994,
title = "Combining multiple tracking modalities for vehicle tracking at traffic intersections",
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.",
keywords = "Blob tracking, Joint Probabilistic Data Association filter, Mean Shift tracking, Stop-and-go traffic",
author = "Harini Veeraraghavan and Papanikolopoulos, {Nikolaos P}",
year = "2004",
month = "7",
day = "5",
language = "English (US)",
volume = "2004",
pages = "2303--2308",
journal = "Proceedings - IEEE International Conference on Robotics and Automation",
issn = "1050-4729",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Combining multiple tracking modalities for vehicle tracking at traffic intersections

AU - Veeraraghavan, Harini

AU - Papanikolopoulos, Nikolaos P

PY - 2004/7/5

Y1 - 2004/7/5

N2 - 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.

AB - 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.

KW - Blob tracking

KW - Joint Probabilistic Data Association filter

KW - Mean Shift tracking

KW - Stop-and-go traffic

UR - http://www.scopus.com/inward/record.url?scp=3042620349&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=3042620349&partnerID=8YFLogxK

M3 - Article

VL - 2004

SP - 2303

EP - 2308

JO - Proceedings - IEEE International Conference on Robotics and Automation

JF - Proceedings - IEEE International Conference on Robotics and Automation

SN - 1050-4729

IS - 3

ER -