[Don Roberts] Here at S&T we're developing a number of tools among them a video
algorithm capable of automatically detecting bags left behind
and tagging individuals who left those bags. We're also developing a
suite of video forensic tools that allow video surveillance systems to work more effectively
and efficiently.
[Robert Sealock] Today we are at New Carollton Metro station. We are testing some of camera
algorithms by placing bags in a variety of locations.
If you have a fairly well performing commercial algorithm that alerts
maybe only 60 times an hour, once a minute, when you multiple that by the
50,000 or so video camera in a mass transit system.
The number of false alerts is quite overwhelming. Its inconceivable
that even a fully staffed operations center is going to be able to field that many events over the course
a day. We are looking at a false alert rate that is appreciably lower
then what's commercially available and basically baked into the infrastructure.
and the video analytics and the video management systems that presently exist.
[Shawn Doody] The FOVEA program allows us to
be able to identify a package that's been left behind and then
to figure out who left it it behind and then start
to track that person and determine weather or not this is a
threatful situation and deploy proper resources to keep the people safe.
[Marianne Deangelus] So we're helping make existing video surveillance
systems more efficient, more effective, by giving users tools that help them get through the video faster.
One of the tools within FOVEA is a tool that we call jump-back and it lets a user
highlight an abandoned object, simply draw a box around it and jump back
to when that object first appeared. And from there the user can investigate what are the
circumstances around it. So for instance it's jumped back to when the
person left the bag. We can actually begin to bookmark the person in the video
and follow as they move throughout the station.
So here we're actually piecing together information from different video cameras.
Now once a user actually followers a person throughout the station
and understands where they have come from and where they have gone to
they could then reconstruct that video and stitch all of those pieces together
into once final video.
[Music]
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