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

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


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.

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