Quote:
Originally Posted by marshall
These boards can do substantially more than that. Vision processing with OpenCV is capable of doing object recognition (Think: looking for and recognizing the bumpers of other robots and playing automated defense: "No, I didn't pin them for 5 seconds, it was exactly 4.99 seconds and I have logs to prove it"  ). If you are going to use this board then I suggest you plan on doing something above and beyond the basic vision challenge of tracking an object by color alone or determining if a goal is simply hot/cold. Granted, I'm a bit of an ambitious dreamer and not always a realist but my students keep surprising me. |
To expand on this:
There are a number of ways of doing object recognition, the most common method is by thresholding based on color. The act of classifying every pixel into, usually, 2 groups: foreground and background. It is an optimization problem if you get down to the roots of it.
Pictorial representation
This works method usually works for game piece detection, as well as target detection. A problem occurs when you threshold by color for bumpers. Take the 2014 game for example. The balls were blue and red. The bumpers are blue and red. There is not a strict color requirement for bumpers, however. Yes, the have to be red and blue, but they can be different shades.
So, this leaves a program that learns what a bumper is. There are a few ways to do this, but all are very computationally intensive. Facial recognition programs uses these types of algorithms. One such algorithm is the haar cascade. What this requires from you is to take as many pictures of bumpers as possible, then train your program on the data set. To get the best results, you'd have to go around at competition and take as many pictures as possible of every robot. Then you have to train the program, which it is not uncommon for it to take several hours to do so.
I personally believe that there needs to be an objective (aerial) camera in order for the level of autonomous play to increase.
End of irrelevant rant.