Predator Object Tracking Algorithm

Post by sp332 » Sun, Apr 03, 2011, 8:49 PM UTC

6ren wrote: post #23

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…

Post by newhouseb » Sun, Apr 03, 2011, 9:05 PM UTC

extension wrote: post #22

ChuckMcM wrote: post #16

As this doesn't seem like an April fools joke (some of the papers were published last year :-)) its interesting to think about it in the context of what it might change. That being said I don't doubt for a minute that the university has locked up as much of the technology as possible in patents but that is another story. We can speculate about what it will be like in 20 years when people can do this without infringin…

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.

Post by StavrosK » Sun, Apr 03, 2011, 9:19 PM UTC

newhouseb wrote: post #18

jallmann wrote: post #14

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…

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…

Post by Groxx » Sun, Apr 03, 2011, 9:24 PM UTC

newhouseb wrote: post #33

extension wrote: post #22

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…

Post by [deleted] » Sun, Apr 03, 2011, 10:00 PM UTC

BoppreH wrote: post #13

The face recognition part was too good for not picking up the face of other people. Or was it detecting just the most similar face? But facial recognition aside, the uses are endless. If it can be brought to the same level Kinect drivers are at, but with finger tracking and no custom hardware , this could change everything.