Name: ml5.js 用PoseNet進行姿態估計。 (ml5.js Pose Estimation with PoseNet)
Uploaded: 2021-01-14T10:17:50.000Z
Duration: 14 min 25 s
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Hello, and welcome to another Beginner's Guide to Machine

And I'm going to look at what PoseNet is,

how to use it with the ml5,js library with the p5.js library,

and track your body in the browser in real time.

The model, as I mentioned, that I'm looking at,

use, the first question you probably want to ask is,

And then as output, it is going to give you

In addition to each of these xy coordinates,

it's going to give you a confidence score for each one.

And what do all these xy coordinates correspond to?

They correspond to the keypoints on a PoseNet skeleton.

Now, the PoseNet skeleton isn't necessarily

is 17 points that you can see right over here,

from the nose all the way down to the right ankle, that it is

trying to estimate where those positions are

on the human body, and give you xy coordinates,

One other important question you should ask yourself and do

some research about whenever you find yourself using

a pre-trained model out of the box, something

that somebody else trained, is who trained that model?

PoseNet is a bit of an odd case, because the model itself,

There's examples for it in TensorFlow and tensorflow.js

But the actual code for training the model,

from what I understand or what I've been able to find,

A data set that's used often in training models

around images is COCO, or Common Objects In Context.

of people striking poses with their keypoints marked.

was used exclusively for training PoseNet,

whether it was used partially or not at all.

for finding out as much as you can about the PoseNet model

in fact there's a PoseNet 2.0 coming out.

I would also highly suggest you read the blog post "Real-time

Human Post Estimation in the Browser with TensorFlow.js"

by Dan Oved and editing and illustrations from Irene

So there's a lot of excellent background information

about how the model was trained and other relevant details.

If you want to learn more about the COCO image data set,

I also would point you towards the Humans of AI project

by Philip Schmidt, which is an artwork, an online exhibition

that takes a critical look at the data in that data

If you found your way to this video, most likely,

you're here because you're making interactive media

use to do real time body tracking very quickly

It's frankly, pretty amazing that you could do

So one way to get started, which in my view,

is one of the easiest ways, is with the p5 Web Editor,

in the p5.js library, which very, so I have a sketch here

Also want to make sure you have the ml5,js library imported,

and that would be through a script tag in index at HTML.

Once you've got all that set up, we're ready to start coding.

So I'm going to create a variable called PoseNet.

I'm going to say PoseNet equals ml5.posenet.

All the ml5 functions are initialized the same way,

by referencing the ml5 library dot the name of the function,

Now typically, there's some arguments that go here.

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ml5.js 用PoseNet進行姿態估計。 (ml5.js Pose Estimation with PoseNet)

recognize

multiple

accurate

confidence