Kenya is home to some of the most
diverse and beautiful wildlife in the
world but unfortunately also some of the
most endangered Kenna technology like
artificial intelligence really helped
save these majestic creatures
one team scientists think so I'm Arjun
dot and this is Imai
[Music]
I'm in Kenya for the great Grevy's rally
we're using people driving around all
over the country to count
Grevy's zebras 90% of them are in Kenya
they're highly critically endangered
we've got conservation managers working
with County government officials
pastoral livestock owners and the middle
class from Nairobi all converging being
given their cameras given the maps of
the areas that are patrolled and then
for two days they go out and they snap
as many pictures as they can and as the
number of herds of cows sheep and goats
have increased the people have dominated
the landscape and have pushed the
Grevy's zebra into more marginal grazing
habitat but they've also kept them away
from water during the daytime which
means it gets more predictable when they
drink at night for the Lions to eat them
and it also means they can't get water
during the day to cool and evaporate to
keep their body temperatures normal and
so as a consequence the Grevy's zebra
was starting to be out competed by the
livestock in order to understand the
dynamics of populations you actually
have to follow the fate of individuals
so in order to study individuals you
have to recognize every individual and
that's where wild book comes in wild
book is a software platform which allows
us to use computer vision and artificial
intelligence to identify down to
individual animals not only zebra
giraffe turtle a whale but zippy the
zebra and Terry the turtle and Willy the
whale to most people one zebra looks
like another so how does wild book help
the scientists tell them apart each
zebra is uniquely marked by its natural
barcode and the algorithms that the
computer scientists have developed is
they can look at the pixels and the
local neighborhood of pixel change the
rate of change between the black and the
gray and the white identifies hotspots
on the animal and once we get a map of
those hotspots on each individual we can
then compare any new image against the
images in the archive and that allows us
to determine whether is an animal we've
seen before who's been named or whether
it's a new animal that gets a new name
if it's an animal we've seen before then
we have a historical record where was it
who was it with we can do social network
analysis and what was it doing we have
the metadata from the picture or from
the ecological measurements you got one
hundred thousand images of zebras how do
you even start to ask is this zebra in
any one of those pictures in our archive
from the 2016 grade grave is rally about
200 people were taking pictures and we
got 40,000 images of zebras this time we
have more than 700 people driving around
taking pictures we expect over 200,000
pictures of zebras that's an amazing
amount of photos I wonder how do they
sort through them all and then identify
individual animals each image is
processed through a series of
convolutional neural networks and
matching algorithms the first network
determines whether there are any animals
of interest in this case zebras the next
set of networks localize each animal in
its own sub region and classify the
species at this point background
segmentation is performed on the sub
region to generate a rough mask of the
zebra once this is done key points are
extracted from the zebra and feature
descriptors are built these descriptors
serve as a sort of fingerprint that can
be compared for similarity against
previously identified individual animals
in a large database the scores from this
comparison allow the system to decide
which animals are the most likely
matches when the match has high enough
confidence it's accepted immediately
matches that have lower confidence are
shown to a human expert who then makes
the final decision the problem of
finding animals in hundreds of thousands
of images and classifying their species
that is a deep learning problem the
identification of individual animal is
in some way orthogonal to this training
any deep learning model takes a long
time with GPU we can reduce it from
hours to minutes deploying the model
without GPUs it can take seconds per
image which is too long when you're
talking about hundreds of thousands of
images with GPUs we're down to
milliseconds once all this data is
collected and processed how is it used
to help the Grevy's zebras were there
any other personal cameras once we have
our census estimate the first thing a
policymaker is going to want to
well how accurate is that number if it
is consistent then they'll invest
resources to maybe protect the corridor
so the animals can get from food to
water they may be able to work with
pastoralists to leave areas fallow so
the Grevy's zebras can fatten and the
babies can survive given that these
species are mostly found in
conservancies I think this would be very
helpful for us as an organization to get
to know the statistics and then that
will help us engage more with the
government to show them whether these
species are declining or they are
growing and help us to come up with a
strategy on how we can conserve these
issues while book is clearly a great
tool for helping the Grevy's zebras with
their distinct individual markings but
can it be used for other endangered
animals our current technology can be
used to identify any striped spotted
wrinkled or not species there are
animals that we would like to be able to
identify that we would like to count and
track non-invasively one of them is the
savannah elephant if we do not do
anything the poaching rate is so high
that within just two decades we might
not have any more elephants in Africa we
hope that we can push our current
technology to identify elephants
you