3 ms·

That's an interesting way to go about it but would the texture of the road surface be consistent enough and reliable enough to make those calculations? This is

That's an interesting way to go about it but would the texture of the road surface be consistent enough and reliable enough to make those calculations? This is the kind of stuff I'm talking about. I definitely do not have the confidence to do that. It's why machine learning is so scary to me. We're basically saying "I can't figure out all the possible permutations of this so let's throw them at a computer and hope it can do a better job".

jazzyjackson 5y ago
Can you tell me more about what kind of road (in)consistency you’re describing? It’s fun for me to think of how to improve this strategy. I am similarly opposed/nervous around machine learning, seems impossible to debug weird decisions the black box makes, I would prefer a traditional, deterministic computer vision approach. Basically I want to segment incoming images into a grid and do a cosine similarity score between each cell and its neighbors one frame ago, to detect which direction and how quickly all the textures are moving. So I think it should be robust to whatever the road texture is, provided its not pitch black like wet asphalt.
avereveard 5y ago
> consistent enough you want the opposite, if it's consistent, then it's more difficult to gauge it's motion just by finding similar zones. think on mouse on glass or metal, it's too uniform for the sensor to detect motion. anyway: paper https://dspace.mit.edu/bitstream/1721.1/6298/2/AIM-476.pdf https://dspace.mit.edu/bitstream/1721.1/6298/2/AIM-476.pdf lecture: https://www.youtube.com/watch?v=0BVZDyRrYtQ https://www.youtube.com/watch?v=0BVZDyRrYtQ usually circular motion is used, but linear works just as fine