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

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

doi:10.1109/MRA.2005.1411419

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

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

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

13 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 language	English (US)
Pages (from-to)	50-58
Number of pages	9
Journal	IEEE Robotics and Automation Magazine
Volume	12
Issue number	1
DOIs	https://doi.org/10.1109/MRA.2005.1411419
State	Published - Mar 2005

Keywords

Intelligent transportation systems
Robotic vision
Segmentation
Vehicle tracking
Video analysis

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1109/MRA.2005.1411419

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

@article{5863276d38c34d2b840aa164fd8072e4,

title = "Spatiotemporal vehicle tracking: The use of unsupervised learning-based segmentation and object tracking",

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.",

keywords = "Intelligent transportation systems, Robotic vision, Segmentation, Vehicle tracking, Video analysis",

author = "Chen, {Shu Ching} and Shyu, {Mei Ung} and Srinivas Peeta and Chengcui Zhang",

note = "Funding Information: For Shu-Ching Chen, this research was supported in part by NSF CDA-9711582, NSF EIA-0220562, and the Office of the Provost/FIU Foundation.",

year = "2005",

month = mar,

doi = "10.1109/MRA.2005.1411419",

language = "English (US)",

volume = "12",

pages = "50--58",

journal = "IEEE Robotics and Automation Magazine",

issn = "1070-9932",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "1",

}

TY - JOUR

T1 - Spatiotemporal vehicle tracking

T2 - The use of unsupervised learning-based segmentation and object tracking

AU - Chen, Shu Ching

AU - Shyu, Mei Ung

AU - Peeta, Srinivas

AU - Zhang, Chengcui

N1 - Funding Information: For Shu-Ching Chen, this research was supported in part by NSF CDA-9711582, NSF EIA-0220562, and the Office of the Provost/FIU Foundation.

PY - 2005/3

Y1 - 2005/3

N2 - 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.

AB - 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.

KW - Intelligent transportation systems

KW - Robotic vision

KW - Segmentation

KW - Vehicle tracking

KW - Video analysis

UR - http://www.scopus.com/inward/record.url?scp=17844373569&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=17844373569&partnerID=8YFLogxK

U2 - 10.1109/MRA.2005.1411419

DO - 10.1109/MRA.2005.1411419

M3 - Article

AN - SCOPUS:17844373569

VL - 12

SP - 50

EP - 58

JO - IEEE Robotics and Automation Magazine

JF - IEEE Robotics and Automation Magazine

SN - 1070-9932

IS - 1

ER -