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The Australian Centre for Visual Technologies has a project which aims to help reduce the manpower required to actually catch the person responsible, once you've spotted the crime occurring (amongst other things), which will allow you to follow a person of interest across many video streams until either you get a good, high-res image of their face, or they go close to a policeman (although that would require manual intervention in the current version of their system). The beauty of their system is that it primarily relies on straightforward object tracking, not biometrics, which allows it to work with much lower resolution cameras than biometric-based approaches, provided there is enough space around the target (which would occur even on less busy urban streets), greatly reducing the cost of the system. It can also be added to existing systems, the network can be expanded without human intervention at the software level, and the system lends itself very well to distributed computing.

(There are two of the key papers at the bottom of the linked page.)(Disclaimer: I'm not a member of the centre, but I think I've found an interesting thesis topic related to one of their other projects, so I'm not entirely unbiased. :) )

My AI page which has several links that go deeper to older write ups is at www.fossai.com

Basically I say that the better computer vision you make, the better software you can write advanced bots leading up to AI. I see AI as being something we'll naturally get to even if no one makes an effort to it: Our 3d cards are getting better, video games are making better 3d worlds, memory is getting bigger, and computer speeds are getting faster. Even if you couldn't hold AI on a current computer's memory, you have wireless internet that links up with a supercomputer to make thin client bots. So there really isn't anything in current technology that is holding us back except computer vision.

Now I am not so good in the computer vision field, but as I see it(excuse pun), there are two ways to do vision.

1) Exact matching. You model an object in 3d via CAD, a Pixar style, or using Video Trace First you database all the objects that your AI will see in its environment then you make a program that identifies objects it "sees" with computer cameras and laser range finding devices. So then the AI can reconstruct its environment in its head. Then the AI can perceive doing actions on the objects.

I'm currently not in the loop here. I can't talk to anyone at Video Trace because I'm just a person, and they don't want to let me in on their software. So I can't database my desk. So I can't make the program that would identify things.

2) Even better than exact matching is similar matching. No two people look alike besides twins, so you can't really just database in a person and say that is a human. And as humans go, there are different categories such as male and female, and some are androgynous so we can't tell their sex. Similar matching has a lot of potential in its ability to detect things like trees and rocks. Similar matching is good at an environment that is tougher to put into exact matching situations. So just from this information alone, I wouldn't start on similar matching unless you had exact matching working in a closed environment. I'm not saying that some smart individual couldn't come up with similar matching before exact matching. I'm just saying that for myself, I'd start with exact matching, and then extend it with similar matching. There are a lot of clues you can pick up on if you know exact locations of things.

And then once you have singular location vision working, you can add multi point vision working. Multi point vision would mean that if you had more robotic eyes on a scene that you'd gain more detail about it. You could even get as advanced as conflict resolution when one robotic eye thinks it sees something, but another thinks it is something different. The easiest way to think of a good application for this would be if you had a robotic car driving behind a normal semi trick and another robotic car infront of the semi. The robotic car in the back can't see past the semi to guess traffic conditions of when the semi will slow down, but the car in front of the truck can see well, so they can signal to each other information that would let the car in behind the semi truck follow closer. If you get enough eyes out there, you could really start to put together a big virtual map of the world to track people.

I wouldn't say AI that learns like humans is desirable. After all, you'd have to code in trusting algorithms to know who to listen to. I'd say AI that downloads its knowledge from a reliable source is the way to go. It is easy to see: Sit in class for years until you learn a skill, or download it all at once like Neo on training seat.

Anyway, you can do a lot with robots that have good computer vision. Thething that has to be done next is natural language understanding. So far we've discussed the AI viewing a snap shot of a scene and being able to identify the objects. Next you'll have to introduce verbs and moving.

How right you are. My apologies to the Autralian Centre for Visual Technology.

A bit more DYI but cool.

I think the description is a little bit wrong cause it makes people think this software actually is very automatic, when in fact it just do what Blender and other softwares do, but with videos instead of images, what should not be difficult to add in Blender also. You could check the video here to see that is very manuall http://www.acvt.com.au/research/videotrace/ The only advance to me is the automatic UVmapping.

Don't link to blogs.
http://www.acvt.com.au/research/videotrace/

wow, what a terrible link.

A quick search turns up the project homepage http://www.acvt.com.au/research/videotrace/