Okay, triggering the camera. Lights on on the camera. So here we go.
So now it's sending the at commands. That's what we're seeing here, right?
Mm-hmm. , we had to stop poachers before they killed wildlife.
That was the whole motivation behind all of this technology. And to do that,
we would have to have basically a burglar alarm for a national park.
And that's what we created.
Carolina Wren calling. It's the tea kettle song.
We'd be doing this if we didn't have financial support,
if we didn't have the organization behind us. This is, you know,
our goal to save wildlife.
And we recognized the problem that there was no technology that was out there
that could stop the poaching before they killed the animals.
A as Steve often says, it was never his intention of inventing a camera,
but there wasn't a camera that met our technical specifications,
so we had to do it.
I've always felt that technology, if applied appropriately,
could be an answer to solving some of the big challenges we face in
conservation.
So we're joined in our testing of Trail Guard by a beautiful forest bird.
The Hairy Woodpecker
Trail Guard is the first tiny AI embedded camera
alert system that is very low power and offers very long battery life.
They're.
All are about 255,000 parks in the world,
and about 80,000 of them are under threat, whether it's poachers,
whether it's illegal loggers, miners, et cetera.
So it should take its normal time
A minute roughly.
The larger the park,
the more difficult it is to manage with the rather skeletal crews of
rangers that they often have in these parks. Like let's take for example,
the Serengeti National Park where we've deployed Trail Guard at one point.
They have 155 rangers to protect an area the size of the state of
Maryland that we're in right now.
What if we could have several hundred of these in a park? Then all of a sudden,
those 155 rangers become as effective as 1500 or maybe even
15,000 rangers.
So it's what in the military we call force multipliers. There is,
we got it. Success, Steve.
So there's me in the bounding box proving that the camera
works.
Did you forget how to howl?
[Grace barking back at Eric]
I joined Resolve about nine years ago after 26
years at World Wildlife Fund, where I was chief scientist. At Resolve,
I set up a new program called the Biodiversity and Wildlife Solutions
Program. And out of that,
we started experimenting with new kinds of technology for conservation.
The reason that I'm so passionate about conserving endangered wildlife
probably stems from the realization that they have every right to be here too.
And we shouldn't be the ones who decide whether they're going to
flourish or continue to eke by or perish from the earth.
It's the multiplicity of species,
which makes life so much more valuable.
So I don't want to be the only species around on the planet.
So all this is the communication with the network, the LT network,
and then finally it shuts down at this point.
Uh, 11:30, 11:42. So the last 1, 11:41. Oh, it just took another one.
Perfect. It transferred the data.
We will measure the quiescent current right there where it's shut off.
Most important things for a remote sensor is to be very
low power, uh, because you can't have people going out and changing batteries,
particularly like in reserves where there's serious poachers and it could be,
you know, risky to the Rangers.
So this system has to be as autonomous as possible. Basically,
everything in the system is off, except in the camera.
We have this passive infrared motion sensor. We have the ambient light sensor,
and we have the micro controller that's buried in here,
always on, but it's super low levels, like, um,
five to 10 micro amps. So almost nothing. Okay, so it's,
it's 7.87 micro amps, which is perfect. So it's between five and 10.
The camera and the communications unit is sound asleep,
basically powered off and drawing almost no current.
And that's exactly what we want. So this, this unit is ready to go to the field.
So Charlie, if you wouldn't mind being our, our Guinea pig , our,
our our human object class. Hey, guys, lights off.
So should have taken your picture and detected you as a human being.
I'll tell you what probability you are as a human if you'd like to know
The probability!? ? Yeah. Okay. Excellent. Okay,
let's see what your probability was.
And then it filters out any false positives. And,
and there you are looking pretty snazzy. The probability that Oh,
is really good today. Yeah, you put on, you put on your human suit.
Look at you are 0.99900, heck yeah. .
That's good. What, what part of you is not human?
It's a Cooper's Hawk.
Human wildlife conflict is emerging as in many ways the most
serious problem of wildlife conservation around the world. Okay,
so we've got five minutes until the tiger comes out. Okay?
One of the things that we want to do is to mitigate,
reduce human wildlife conflict, whether it's tigers,
whether it's wolves or grizzly bears or elephants coming into croplands.
And the first thing that you have to do in order to promote coexistence is you
have to be able to detect the animals in the first place.
And that's where trail guard comes in.
So we've got three different cameras going, uh,
different versions of the AI computer vision chip.
This one is our old standby.
And the inference will be done on the myriad two chip from Intel. And here,
here she comes. Here she comes. Let's go.
There's the tiger. Oh wow. Zoom in on it. Super.
So that's the AI pulling the tiger out and identifying it as a tiger. Wow.
So CVEDIAs tiger detector is working beautifully in our system.
So actually I've been working on this for more than a quarter of my life.
But had I had the right tools at the right time,
it would've come to fruition much sooner. And in the course of that,
it would've saved countless animals.
One of the best things that can help, uh,
any engineering effort is using the right tools.
A lot of the people that are interested in helping animals and using
technology to do so are distributed around the world and different time
zones, different languages. So a tool like Altium,
particularly Altium 365, with its collaborative capabilities,
it allows real time communication and exchange of ideas. It's like having our,
the rest of our engineering team and the next cubicle rather than across the
ocean.
We often get from the smartest people. The question.
So what effect have you had? Like, how can you measure it?
In Dua Tiger Reserve in the, in the north, in Uter Pradesh state,
we were setting up our camera system to monitor tigers. And that evening,
a group of poachers walked by and we caught them on camera,
and then the arrest went out for them.
We've been deploying our system in two parks in Zambia, in Africa,
and in both of those, we've detected poachers, uh, almost every day.
So it's clear that Trail Guard has this tremendous role to play as,
as a burglar alarm, as an early warning system.
It's a reward for all the time that you spend trying to make this happen.
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