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動画の字幕をクリックしてすぐ単語の意味を調べられます！
単語帳読み込み中…
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Hello.
My name is Martin Kronberg, and
this is the IoT Developer Show
season two.
During our break, we've been
busy reworking the show,
so think of this less
like a sequel and more
like the gritty reboot.
We'll be coming
out with a new show
every other Wednesday for
the rest of the season.
Moving forward, the
IoT Dev Show is going
to have an all new format.
We're going to be taking deep
dive looks into specific IoT
technologies over the course
of multiple episodes grouped
into a series.
Last season, I gave you guys
a broad overview of all the
cool Intel IoT tech with
some special guests.
And this season I'll be
up here, a one man show,
leading you through deeper dives
into the technology, the tools
available for developers,
and demos that have
been built using those tools.
For the first
series of episodes,
we're taking a look at Open
Visual Inference and Neural
Network Optimization
Toolkit, or more
simply, the OpenVINO Toolkit,
which gives developers
the power to create cutting
edge AI powered computer vision
applications.
Intel computer
vision technologies
have grown over the
last year and have
combined with Intel's
Deep Learning Toolkit
to form OpenVINO.
But before we get to
the details of OpenVINO,
let me show you
guys a cool demo.
Here is the head position and
emotional state detector demo.
It's running on a
brand new IEI Tank,
which is a coupe
piece of hardware
that we're going to
be covering later on.
I'm using a couple of
deep neural network models
to detect the position
and orientation
of my face, an
analysis of my gender,
my age, and even my mood.
All this is running at
the edge on the tank
and running at over
120 frames per second.
And that's what
OpenVINO's all about--
leveraging powerful neural
network processing of video
as fast as possible
on Intel architecture.
Want to learn more about
how this demo works
and how you can build
something like this yourself?
Well, stay tuned, because
we're going to cover
all of that and much more.
First of all, let's
do a quick overview
of traditional computer
vision versus deep learning.
In traditional computer
vision, an image
is analyzed using
programmatic methods.
For instance, if we're
looking to identify a face,
one method uses Haar
cascade classifiers.
This method relies on taking
the difference of pixel values
in various areas and linking
it to known features,
such as edges, eyes, so on.
We can then say that two
eyes and an oval is a face.
In deep neural
networks, this approach
is radically different.
Instead of telling the computer
of what features to look for--
eyes and so on--
we show the computer
10,000 images
of a face from various
angles, and then it
learns what it looks
like by adjusting
the structure of a
complex, interconnected
network of nodes.
If this sounds like a black box
to you, you wouldn't be alone.
In an article from the
MIT Technology Review
called The Dark Secret
at the Heart of AI,
AI engineer Joel Dudley said,
"We can build these models,
but we don't know
how they work."
But the fact of the matter
is that they do work and work
extremely well.
In fact, with purpose
built deep learning models,
a computer can recognize objects
faster and more accurately
than any human.
But for now, what
we need to know
is that deep learning has two
components-- a training phase,
where the computer learns
to identify objects,
and an inference phase,
where the now trained
model is used to infer the
identity of unknown objects.
Now, with that out of the
way, let's take a look
at what's inside OpenVINO.
It's a combination of tools
for computer vision and AI.
It uses OpenCV 3.3,
which has been optimized
for Intel architecture.
OpenCV can be used
for pre-processing
an image for analysis
and then running analysis
on it, either through the
traditional programmatic
methods or deep neural networks.
OpenVINO also has a custom
inference engine built by Intel
for running deep neural
networks for computer vision.
And inference engine
is what's used
to run the inference
phase of deep learning
that I mentioned earlier.
What makes this
inference engine awesome
is its flexibility
and its performance.
It's made to utilize
both your Intel
CPU, your integrated Intel
GPU, as well as a VPU,
like the Movidius Compute
Stick, or an FPGA,
like the Altera Arria 10.
It's also been optimized to
use the latest and fastest APIs
to access all of
those processors.
Using various processors
for a single task
is called heterogeneous
computing,
and it's part of what
makes OpenVINO so fast.
So how can you start
developing using this toolkit?
Well, we have a ton
of documentation out
there on IDZ and a few GitHub
pages to get you started.
We also have two
developer kits--
the UP Squared AI
vision Development
Kit that can be used
for rapid prototyping,
and the IEI Tank,
which can be used
for more demanding applications
in an industrial environment.
They both come loaded
with all the software
alongside awesome
hardware to help you
get started developing fast.
That's all the time
we have for today.
In the next four
episodes, we're going
to cover all we saw
today in more detail.
I'm going to show you
more awesome demos,
talk about all the neural
net models available,
the IDEs that you can
use, and deep dive
into some of the
reference designs.
We're also going
look at the hardware
and talk about
heterogeneous computing.
Thanks for watching, and we'll
see you guys in two weeks.
コツ：単語をクリックしてすぐ意味を調べられます！

読み込み中…

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Introducing OpenVINO and Computer Vision | IoT Developer Show Season 2 | Intel Software

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alex 2019 年 4 月 26 日 に公開

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