51–60 of 81 posts
Technical details here, with links to relevant papers at the bottom. http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html
Unfortunately you cannot download the source code, the link is disabled. And the GPL license he says he is using requires that the source code must be available for download without any restrictions like "send me an email" or "create an acount". Edit: I sent him an email :)
Earlier quoted context omitted.
Spot on. There's a lot of current work going on that effectively splits computer vision into multiple parallel tasks for better results but uses previously well known techniques (PTAM is another good example). As an aside, I read through the paper and it doesn't look like this could track, say, your index finger separately from other fingers if, for a moment, your hand was occluded. This pretty much bars using this e…
I don't understand why everyone seems to have such a hardon for Minority Report-style systems. Gorilla arm pretty much rules that out from the start, and a tablet is more natural anyway. A trackpad with a separate screen would be optimal (so you don't have to look at your hands).
By now, it's practically a 145-minute tech concept video with a plot starring Tom Cruise.
Earlier quoted context omitted.
Unfortunately you cannot download the source code, the link is disabled. And the GPL license he says he is using requires that the source code must be available for download without any restrictions like "send me an email" or "create an acount". Edit: I sent him an email :)
He's licensing the code to you under the GPL. He's free to use his own code however he likes.
The key thing seems not to be the specific algorithm, but the idea of using images obtained during performance for training - an algorithm that can do that. An early prototype algorithm, with lots of room for tweaking - and there are likely radically different learning algorithms, as yet untried or undiscovered, that work better. It seems that in the past, performance images has been religiously separated from traini…
The tracking was incredibly good. The first seconds of each example are a little off because the algorithm doesn't have ANY training data to work with. It's 100% ad-hoc. The tracking improved during the mouse pointer example (which I found incredibly impressive). The point he was making during that example was that it learns the different scales/rotations of objects on the fly and tracking improves automatically.
btw: my comment originally included praise for his work - but I thought his merit was obvious and that it distracted from my comment's point, so I deleted it. Instead, I'll just note that the first telephone also had lots of room for improvement - practically, the first of anything does. The cool thing was the idea of the telephone, and then making it real. How good it was is irrelevant compared with that it became real. It doesn't take away from the immense task of doing something that had not been done before or even imagined. Quality is not as crucial, because once you have the basic thing, it's (relatively) easy to iterate to improve it. I think having the idea and making it real deserves far far greater admiration than the quality of the prototype algorithm and implementation. Just as with the telephone.
This is massively ground breaking. You'll get it if you've used motion tracking on several game interfaces and had to make perfectly white backgrounds with bright lights to make it work. This is incredibly accurate - really game changing stuff.
>This is massively ground breaking. Sounds like you're used to bad algorithms. I think there is a serious disconnect between the state of the art in computer vision and what's used in industry. The demo was cool, but the techniques are not that revolutionary. From a cursory glance through the papers, it is basically AdaBoost (for detection) and Lucas-Kanade (for tracking), with a few extensions. Not to discount the g…