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Proceedings Paper

Dynamic camera calibration to estimate mean vehicle speed
Author(s): Suree Pumrin; Daniel Dailey
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Paper Abstract

In this paper, we present an algorithm to estimate mean vehicle speed from un-calibrated roadside cameras. The algorithm presented creates a new virtual speed sensor that leverages the large numbers of low quality cameras already installed by transportation agencies. The calibration problem considered here is complicated by the ability of the operation staff to pan, tilt, and zoom the un-calibrated roadside cameras. It is in this framework that we present an algorithm that: (1) performs a simplified dynamic calibration and (2) estimates mean vehicle speed. In the work presented, we wish to estimate the mean vehicle speeds and we will demonstrate that a simplified, and perhaps less accurate form of calibration is adequate to make an accurate mean speed estimate. We use 10-second video sequences as training sets to dynamically calibrate the camera. Our proposed method detects moving vehicles in a set of consecutive frames. This information, together with a mean vehicle dimension, allows us to estimate a scaling factor for a one-dimensional transformation between motion in the image and motion in the earth coordinates. As a result, our algorithm requires the estimation of fewer camera calibration parameters. We validate our algorithm with both simulated data and real world traffic scenes.

Paper Details

Date Published: 22 May 2002
PDF: 12 pages
Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); doi: 10.1117/12.468010
Show Author Affiliations
Suree Pumrin, Univ. of Washington (United States)
Daniel Dailey, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 4667:
Image Processing: Algorithms and Systems
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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