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

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10.1109/MRA.2005.1411419

Cite this

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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",

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doi = "10.1109/MRA.2005.1411419",

language = "English (US)",

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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

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

Abstract

Keywords

ASJC Scopus subject areas

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