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.
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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.
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