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Proceedings Paper

Vehicle detection using Gaussian mixture model for infrared orientation code image
Author(s): Nami Hirata; Haruhisa Okuda; Makito Seki; Manabu Hashimoto
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Paper Abstract

This paper describes an approach to the detection of vehicles in infrared images. Stable vehicle detection is important for future intelligent transport systems and is generally done by background subtraction and object modeling. To avoid the daylight-dependent and weather-dependent influences of varying illumination in visible images acquired with conventional ITV cameras, some researchers have been using infrared (IR) images. IR images make it easy to extract foreground vehicle regions from background scenes, but their lack of clarity make object modeling difficult. We therefore propose a method that describes the internal pattern of each vehicle by using Gaussian mixture models (GMM) in the orientation-code image (OCI) space. Each pixel of an OCI has information about the maximum-gradient orientation of the IR image, not intensity information. Gradient orientation information does not depend on contrast and can describe the internal pattern structures of objects even in unclear IR images. We use the GMM to describe the topological structures of the internal patterns of vehicles. This approach can also eliminate the influences due to small differences between patterns. Evaluation tests with actual infrared video sequences have proved that the proposed algorithm provides stable vehicle detection.

Paper Details

Date Published: 6 December 2005
PDF: 8 pages
Proc. SPIE 6051, Optomechatronic Machine Vision, 605104 (6 December 2005); doi: 10.1117/12.648770
Show Author Affiliations
Nami Hirata, Mitsubishi Electric Corp. (Japan)
Haruhisa Okuda, Mitsubishi Electric Corp. (Japan)
Makito Seki, Mitsubishi Electric Corp. (Japan)
Manabu Hashimoto, Mitsubishi Electric Corp. (Japan)

Published in SPIE Proceedings Vol. 6051:
Optomechatronic Machine Vision
Kazuhiko Sumi, Editor(s)

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