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Hi.
I'm Priyanka Bagade.
I'm a developer
evangelist at Intel.

I train developers on the
latest Intel IOT technologies

through workshops, hackathons,
and training videos.

In this video series, we explore
Intel's smart video tools

for computer vision
applications.

[MUSIC PLAYING]
With the internet of things,
more and more systems

are getting connected
to the internet, where

we can analyze the sensor
data to monitor and control

the systems.
Cameras are one of the
most important sensors.

The video application
opportunities are endless.

For example, advanced medical
research, personalized health

care, smart transportation,
smart cities, manufacturing,

retail, or supply
chain management.

These industries rely on
video for critical insights

and competitive growth.
Considering such a
large amount of data

is generated by these
systems, deep learning

seems to be a more
robust solution

for video analytics over
traditional computer vision.

Deep learning can help to
extract meaningful information

from the available data.
For example, when
processing images or videos,

it can detect objects,
faces, and emotions

from the millions of
pixels of the image.

Intel has been working
on solutions for video

to understand developers needs.
We then address those needs
using different platforms,

such as smart cameras,
video gateways, NVRs,

and data centers.
Additionally, we offer tools,
such as the OpenVINO and Media

SDK, to get accelerate
video analytics at edge.

In this video series, we go into
details of the OpenVINO Toolkit

to do optimized inference at
the edge for computer vision

applications.
In the second video
of this series,

we introduce you
to OpenVINO Toolkit

to do video analytics
at the edge.

We talk about components
of the OpenVINO Toolkit

and the new programming
model to deploy application

on a range of silicon by Intel.
The third video dives
into the model optimizer,

which is one of the main
components of OpenVINO

Toolkit for model conversion.
After that, in the
fourth video, we

cover the inference engine,
which provides a unified API

to run the application on
different hardware types.

Then in the fifth
video, we cover

hardware heterogeneity
plugin and how

to run the application
on different hardware

types, such as CPU, GPU,
Movidius Compute Stick,

and FPGA using the
inference engine API.

In the sixth video, we talk
about optimization techniques.

In the seventh video, we
discuss advanced video analytics

using the OpenVINO Toolkit.
In the final video, we provide
a summary of entire series.

We also give you some next steps
and more advanced examples.

At the end of the
series, you should

be able to write an
optimized inference

application at the edge
using the OpenVINO Toolkit.

Thanks for watching.
Watch the next video in this
series for an introduction

to OpenVINO Toolkit.
[INTEL THEME]
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系列介紹| 英特爾®分佈式OpenVINO™工具包| eWorkshop | 英特爾軟件 (Series Introduction | Intel Distribution of OpenVINO Toolkit | eWorkshop | Intel Software)

147 分類 收藏
alex 發佈於 2019 年 4 月 26 日
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