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I understand that it is common to use Hough transforms to detect lines, which is critical in driving to understand lane markers and so on. In my experiments wi

I understand that it is common to use Hough transforms to detect lines, which is critical in driving to understand lane markers and so on. In my experiments with the implementation in OpenCV I haven't gotten good results, especially with noisy detections of lines that aren't there. But here they seem to get good line detection without many false positives, despite difficult properties of the image such as worn down paint and low contrast between the paint and the pavement. Anyone know what they are doing that works so well?

repsilat 7y ago
Hough transforms are "old-school" computer vision. They rely on a know parameter-space, "This is a circle, that is a straight line" and that isn't super robust to noise or to unfamiliar curves. I don't know for sure, but I imagine these days the techniques are closer to what you're more likely to call Machine Learning. Probably neural net classifiers trained on manually tagged data, and maybe augmented with a lot of map data. Maybe with some memory too -- I want to say you'd use a particle filter, but there's probably some newfangled ML technique that does a better job than those.