Dynamic camera calibration to estimate mean vehicle speed

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) estimate's 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.