"Better Eyes"

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Transcript

James Davis: So the camera's actually located on the rooftop and can adjust its pan and tilt to follow the person through the scene. But it also has a zoom adjustment that tries to keep the person the same height all the way through the scene. So right now, they're moving pretty far away here.

So typical surveillance cameras have pan, tilt, zoom capability, but the problem is that most people have to try to re-orient and move the camera around the scene to look at different areas. And, furthermore, the actual camera view that comes out of this, that they show on a TV monitor is actually quite small compared to the actual view coverage area that the camera could potentially see. So, in our system, we've developed a method to automatically generate a wide-angle panorama for the camera and with this you can actually use it as a broader context for this camera so that when they look at the smaller view, now they can place it within this larger region.

Our algorithm is designed such that a person could actually point or click anywhere in this panorama and have the camera automatically point and move to that particular target. So to accommodate multiple surveillance cameras, we take the panorama generated from each camera and place them down onto a large Google map aerial image. And the nice thing about this is that people can look at a single image and see all the areas that can be covered by these cameras. Furthermore, with our system, now the person could actually select or click any location in this Google map and all available cameras in the are can now point and orient towards this location.

So, currently in my research lab, I employ graduate students and undergraduate students. I started off as an undergraduate researcher myself and it's great to have them in the lab. The environment is very dynamic now because we have all levels of students and mentoring happening between the grad students and undergrads and it just turns out to be a very valuable experience.

Matt Nedrich: Most of the work I do involves the smart camera controls and some of the interface for them. The atmosphere here in the lab's awesome. All the people I work with are very helpful. It's been a great learning experience.

James Davis: To deal with the issues of privacy, as we have our own surveillance system here at OSU, we pretty much deal with how people move, where they're existing, but we don't spend any effort on trying to do face recognition or any types of identification. So we try to look at, again, what you're trying to do rather than, you know, who you are. Joe, Mary, or Sue; it's the activity we're mainly concerned about.

This is the new theme in surveillance is how do we detect the abnormal behavior that exists in the scene? So, the biggest issue is how do we learn the trends that are happening? Right now in surveillance systems that exist you can go in and you can select a little region in an image and say, "anytime someone enters this zone, let me know about it" and an alarm would be sent to a security guard. But we'd like to up it to the next level. Rather than annotating all the video and putting hard and fast rules in there, could it just try to sit there and observe the scene over time? Days, weeks, months, even years. And try to figure out what's the typical things that happen in the scene and if I see anything atypical let somebody know about it.

There are several things that are going on in the state, from violent crimes to Department of Homeland Security that we're trying to now deploy it across several cities where a person at a central location could, theoretically, now go look at all individual cities across the state, in real time, all over the place. And try to coordinate the activities that are happening.