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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]

Hi.

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シリーズ紹介|OpenVINOツールキットのインテル配布|eWorkshop|インテル・ソフトウェア (Series Introduction | Intel Distribution of OpenVINO Toolkit | eWorkshop | Intel Software)

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    alex に公開 2021 年 01 月 14 日
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