41–50 of 81 posts
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What if you just... email him and ask for it?
Don't really have a project, just curiousity...
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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…
I bet he isn't using the GPU though.
Also - a crappy webcam actually makes things computationally easier because there's less data to deal with
Perhaps, but lens distortion, motion blur and a rolling shutter don't make things easier.
Anyway, the inventor himself claims a phone implementation is feasible.
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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).
Bah! I was hoping to download the source (from here: http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html ) and check out his algorithm, but he requires you to email him with his project. If anyone knows how the algorithm works, or where it is described in detail, I'd love to read that! Absolutely amazing stuff!
Also, computer vision demos are trivially easy to fake, and it's even easier to make an impressive demo video. You can have the guy who invented it spend a couple hours in front of the camera trying it over and over, then edit it down to three minutes of the system working perfectly. It wouldn't be nearly as impressive when you have an untrained user trying it live, in the field.
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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…
It says he's running on a "Intel Core 2 Duo CPU 2.4 GHz, 2 GB RAM" according to his website I bet he isn't using the GPU though. Also - a crappy webcam actually makes things computationally easier because there's less data to deal with Perhaps, but lens distortion, motion blur and a rolling shutter don't make things easier. Anyway, the inventor himself claims a phone implementation is feasible.
Also, completely agree on how camera blur would worsen the accuracy of said algorithm, I was trying to point out that it would run faster on a lower quality camera (with the caveat that it might not work nearly as well).
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 :)
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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…
Specialized processing hardware != general-use CPU. Face tracking and image stabilization in dirt-cheap cameras is a good example, as is hardware video decoders or graphics cards. If a market emerges, specialized hardware will be built, and it'll be embeddable in just about anything.
I have only a vague understanding of the math behind how it works, yet I'm very successfully using it in an art project I'm playing with. An afternoon's Googling found me the OpenCV plugins for Processing and some face detection examples, and I've got a prototype that really disturbs my girlfriend - I call it "Death Ray" for extra creepiness factor[1] - but I've got a infra-red capable camera mounted on a pair of servos to steer it, and another pair of servos aiming a low power laser. An Ardunio driving the servos and switching that laser, with Processing just "magically" calling OpenCV for face detection in the video stream - _all_ the "heavy lifting" has been done for me - viva le open source!
[1] The thing that _really_ creeps the girl out is when I sit it all on top of the TV, and have it find faces watching the tv and paint "predator style aiming dots" onto peoples foreheads...
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 :)