61–70 of 81 posts
Earlier quoted context omitted.
Don't really have a project, just curiousity...
I certainly intend to drop him an email and see if he's willing to share his stuff with an interested compsci undergrad. It's always worth asking - he's probably just doing it for metrics.
> Can Predator be used to stabilize and navigate a Quadcopter? > That is not straightforward. anyone know why not?
Earlier quoted context omitted.
Depending on how well the computational load can be reduced to hardware small systems could provide for head mounted tracking systems He's clearly developing it on his laptop with a shitty webcam. That's why this is amazing. Screw robotic manufacturing, this is for my phone.
Phone != Laptop. It says he's running on a "Intel Core 2 Duo CPU 2.4 GHz, 2 GB RAM" according to his website. As a good rule of thumb, computer vision runs about an order of magnitude slower (10x) on a phone (like an iPhone) than on a desktop/laptop. Also - a crappy webcam actually makes things computationally easier because there's less data to deal with. In a lot of computer vision algorithms the first step is to t…
Every time something like this comes out, I feel us taking a step away from "video camera mounted on a robot where the eyes should be" and a step toward real perception. I always wonder though, if a computer can one day recognize all different types of hands, could it draw a new one?
He shows how he trained a restricted bolzmann machine to recognize handwritten numbers and how he can run it in reverse as a generative model, in effect the machine 'dreams' about all kinds of numbers that it's not been trained on but nonetheless makes up properly formed legible digits.
Earlier quoted context omitted.
Depending on how well the computational load can be reduced to hardware small systems could provide for head mounted tracking systems He's clearly developing it on his laptop with a shitty webcam. That's why this is amazing. Screw robotic manufacturing, this is for my phone.
Phone != Laptop. It says he's running on a "Intel Core 2 Duo CPU 2.4 GHz, 2 GB RAM" according to his website. As a good rule of thumb, computer vision runs about an order of magnitude slower (10x) on a phone (like an iPhone) than on a desktop/laptop. Also - a crappy webcam actually makes things computationally easier because there's less data to deal with. In a lot of computer vision algorithms the first step is to t…
Wait a year or two and phones will be as powerful as today's laptop.
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.
Keep in mind that there's no report of the processing power required to do this in the video. It very well could be an algorithm that is extremely accurate, but at the expense of many CPU cycles. While it's obvious, remember that for use in games, you have to perform the detection and run the game in realtime. Whether or not this can be done with current hardware is what I'm interested in.
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 :)
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…
Earlier quoted context omitted.
>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…
since you're familiar with the topic, does this look lightweight enough for say, mobile applications, or does it require massive processing power?
So, according to him, it is lightweight enough to run on mobile devices. I'd imagine there are also several optimizations that can be done (leveraging multi-core chips or GPUs, for instance) to make the performance significantly better than the prototype he's demonstrating now. Also, taking into account Moore's Law, we may not be able to run this on today's mobile devices, but surely could on tomorrow's. Given that research is generally a few years ahead of industry, I would expect that, by the time this would come to market, the devices will be more than capable.
Earlier quoted context omitted.
Keep in mind that there's no report of the processing power required to do this in the video. It very well could be an algorithm that is extremely accurate, but at the expense of many CPU cycles. While it's obvious, remember that for use in games, you have to perform the detection and run the game in realtime. Whether or not this can be done with current hardware is what I'm interested in.
On the website he claims it's a fairly standard dual core setup and you can see the number of frames per second in the video. I noticed at one point it was staying around 15fps. It may not work at this point for an FPS (Especially because it would have to be done in conjunction with graphics and other game-related processes), but it would likely be fine with other less fast-paced games.