We need a simple, robust and fast detection method for our application, because the higher precision and sampling frequency of detection we achieve, the better can be the control using optical feedback. We have a static scene with approximately constant illumination and our aim is to track a moving steel ball in this scene. The first attempts were implemented and tested using video frames that were transferred to the host PC over the UDP protocol, because it is practically impossible to save a full-resolution video on the local target hard drive. In addition, we also tested the implemented algorithms in Simulink models on PC using Image Acquisition Toolbox together with a USB webcam. In the time of these experiments, we had only monochrome Camera Link camera in our laboratory. However, the principles can be later broadened to RGB color video signals as well.
Intensity thresholding
Our very first idea was a simple thresholding of intensity image, which would be the simplest and fastest detection method. This method is, however, too simple for our application, because the steel ball has quite strong reflections and both ball and the background contain wide range of intensities that overlay each other. Therefore the object is not separable from the background using only single pixel intensity values. An improvement for the future could be to paint the ball with a color and take advantage of the color camera by e.g. thresholding a single color layer. In addition, with respect to the future application, it would be useful to have balls of different colors to be able to distinguish them.
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| Even for a human, it is difficult to identify the ball in the image precisely |
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