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

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 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

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Cite this

Chen, S. C., Shyu, M. U., Peeta, S., & Zhang, C. (2005). Spatiotemporal vehicle tracking: The use of unsupervised learning-based segmentation and object tracking. IEEE Robotics and Automation Magazine, 12(1), 50-58. https://doi.org/10.1109/MRA.2005.1411419

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