Abstract
Abstract. In this work, a new method is developed for the automatic and accurate detection and labelling of signalized, un-coded circular targets for the purpose of automated camera calibration in a test field. The only requirements of this method are the approximate height of the camera, an approximate range of orientations of the camera, and the object-space coordinates of the targets. In each image, circular targets are detected using adaptive thresholding and robust ellipse fitting. Labelling of those targets is performed next. First, the exterior orientation parameters of the image are estimated using a one-point pose-estimation approach, where a list of possible orientation and target labels are used, along with height, to calculate the camera position. The estimated position and orientation of the camera combined with the interior orientation parameters (IOPs) are then used to back-project the known object-space coordinates of the targets into the image space. These targets are then matched against the targets detected in the image, and the list entry with the best fit is chosen as the solution. This resolves both the detection and labelling of the targets, without the need for any coded targets or their associated software packages, and each image is solved independently allowing for parallel processing. This process accurately labels 92–97% of images, with average accuracy rates of 97% or better, and average completeness rates of 70–95% in imagery from the three cameras tested. The cameras were calibrated using observations from the detection and labelling process, which resulted in sub-pixel root mean square (RMS) values determined for the pixel space residuals.
Highlights
1.1 Literature ReviewAutomatic detection and labelling of imagery is an essential step in the process of calibrating a camera or a multi-camera system
There are many styles of coded targets such as: unique patterns of circles on a target, where the layout of the circles encodes the target (Ahn et al, 2001; Hattori et al, 2002; Knyaz and Sibiryakov, 1998), centripetal encoding where unique sections of a disk surrounding a central circular target are used to encode the target (Niederöst and Maas, 1997; Schneider et al, 1992), algorithms that augment these styles by using colour information (Cronk et al, 2006; Moriyama et al, 2008)
The ellipse axis bounds are approximate estimates determined by the user which are the largest and smallest possible radii for the semiminor and semi-major ellipses of the circular targets in the images
Summary
Automatic detection and labelling of imagery is an essential step in the process of calibrating a camera or a multi-camera system. Calibration requires a large amount of imagery, and manual target detection and labelling are time consuming. A newer algorithm developed by Shortis and Seager (2014) works to the centripetal targets, using straight lines on the boundaries of the targets and converting them to the polar space to encode them. It can only encode 124 possible targets and uses low-cost materials to make this system easy to implement
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