Deep Learning using OpenPose – Learn Pose Estimation Models and Build 5 AI Apps Free Download
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Deep Learning using OpenPose – Learn Pose Estimation Models and Build 5 AI Apps Free Download

More Information
Learn
  • What is Pose Estimation?
  • How to take Pose Estimation further using OpenPose
  • Step-by-step instructions on how to execute OpenPose
  • How to implement your own fall detection app
  • Using OpenPose to count people
  • Dive deeper by using Pose Estimation for yoga pose identification
  • Ensure perfect planking and push-up posture with OpenPose
  • Calculate real-time body ratios
About Pose Estimation is a computer vision technique that can detect human figures in both images and videos. You may have first experienced Pose Estimation if you’ve played with an Xbox Kinect or a PlayStation Eye. Now, imagine developing your own Pose Estimation applications but without the specialized hardware, i.e. just using an ordinary webcam and the power of AI!

Now you can! Whether you want to apply this technology for character animation, video games, assisted driving systems or even medical applications, this course can help you achieve your goal in the shortest possible time.

This course takes you on a journey, assuming you know nothing about Pose Estimation, from learning the fundamentals of the technology to implementing the OpenPose Framework in real-time. You will also explore how to adapt this framework for 5 practical applications:

Fall detection

People counting

Yoga pose identification

Plank pose correction

Automatic body ratio calculation—and much more

As you can see, you will be learning the state of the art in Deep Learning, using AI to understand human actions and behaviors.

All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Deep-Learning-using-OpenPose—Learn-Pose-Estimation-Models-and-Build-5-AI-Apps

Features
  • Learn and implement OpenPose Deep Learning Pose Estimation models and build 5 apps
  • Learn how to execute OpenPose
Course Length 0 hours 41 minutes

 

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About the Author: Ho Quang Dai

I am Ho Quang Dai. Looking forward to receiving positive contributions from readers

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