High Speed Vision System and Object Tracking
Last modified 6 June, 2009
Calibrating the High Speed Camera
Before data can be collected from the HSV system, it is critical that the high speed camera be properly calibrated. In order to obtain accurate data, there is a series of intrinsic and extrinsic parameters that need to be taken into account. Intrinsic parameters include image distortion due to the camera itself, as shown in Figure 1. Extrinsic parameters account for any factors that are external to the camera. These include the orientation of the camera relative to the calibration grid as well as any scalar and translational factors.
In order to calibrate the camera, download the Camera Calibration Toolbox for Matlab. This resource includes detailed descriptions of how to use the various features of the toolbox as well as descriptions of the calibration parameters.
For Tracking Objects in 2D
To calibrate the camera to track objects in a plane, first create a calibration grid. The Calibration Toolbox includes a calibration template of black and white squares of side length = 3cm. Print it off and mount it on a sheet of aluminum or PVC to create a stiff backing. This grid must be as flat and uniform as possible in order to obtain an accurate calibration.
Following the model of the first calibration example on the Calibration Toolbox website, use the high speed camera to capture 10-20 images, holding the calibration grid at various orientations relative to the camera. 1024x1024 images can be obtained using the "Moments" program. The source code can be checked out here.
Calibration Tips:
- One of the images must have the calibration grid in the tracking plane. This image is necessary for calculating the extrinsic parameters and will also be used for defining your origin.
- The images must be saved in the same directory as the Calibration Toolbox.
- Images must be saved under the same name, followed by the image number. (image1.jpg, image2.jpg...)
- The first calibration example on the Calibration Toolbox website uses 20 images to calibrate the camera. I have been using 12-15 images because sometimes the program is incapable of optimizing the calibration parameters if there are too many constraints.
