The idea to try Harris detector originated from attempting to take advantage of the strong reflections on the surface of the ball instead of trying to avoid their influence. Harris detector is a tool that finds significant points in the image, which are usually the corner points. Even though our objects is a ball without any corners, the reflections on its surface form very steep intensity gradients that can be detected by the Harris detector. The background does not have such strong contrasts, so if we set a threshold for minimal value of local maxima of cornerness high enough, the detector only detects the corner points formed by reflections on the ball. The estimate of position of the center of the ball is then computed as median of coordinates of detected significant points. The median is used to avoid influence of a few corner detections that occur in the background, which would strongly bias the estimate if we used mean instead of median.
The algorithm was tested using video frames from monochrome Camera Link camera. The accuracy of the method is strongly dependent on the surrounding and illumination of the experiment. If the illumination is asymmetrical, the estimate is biased. For a symmetrical illumination, the estimate could be quite precise. However, the Harris detector requires computation of intensity gradients in the image, so it spends quite a lot of computing power and therefore is not very suitable for our application.
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| Estimation of ball position based on the significant image points detected by Harris detector (significant image points marked with green squares, ball position with red markers) |
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