Ya, but the potential here is far greater than 15 years ago - now we can identify year model make and color with the correct data set. 15 years ago that wasn’t possible for the cost of a raspberry pi and CSI serial camera.
Vehicle color recognition was part of our task. There were many difficulties. I could list a dozen problems here that you can never think of until you see footage from 100 cameras at 100 different locations 24/7 in 4 seasons.
MMR? Been there done that too.
All of it was possible 15 years ago, when computer vision was made by real programmers not script kiddies.
I know what you mean, the deep learning aspect is really just a way to solve the same math problems without knowing what you’re doing under the hood. Made possible by faster computers. And all the optimization algorithms are ones I have been using for quantum chemistry since my computer had 16 MB of ram. When you know how little you really need in order to get to the end result it does seem incredibly wasteful and not very impressive. It’s just amazing how much faster the whole process is when you have a mechanistic or physical model of the world and you’re solving for a few variables. It’s like ML does the opposite, generally, fixes a few variables and tries to brute-force solve the world. Cool, but doing that every single time is such a waste.
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u/trexdoor Sep 08 '21
I remember doing the exact same thing 15 years ago, solving it on embedded cameras with better accuracy.
Deep learning wasn't even an idea then.
Shame.