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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.

Hello.

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

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    alex   に公開 2019 年 04 月 26 日
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