Gordon  Taylor

Gordon Taylor

1622886480

Simple Smoothing for PoseNet Keypoints

How to use frame averaging as a simple method to smooth keypoint data from PoseNet in p5.js.

Google’s Tensorflow.js  PoseNet model is extremely useful. It returns real-time pose estimation data in the browser from just a webcam, which allows for all sorts of accessible embodied interactions (like  novel navigation modes and  online dance parties) in the browser. Using the  ml5.js library, it’s also pretty easy to  get up and running.

The keypoint data returned from PoseNet is pretty noisy, so it’s helpful to add some smoothing to the points for better user experiences. There are many types of  smoothing algorithms, and they can be _very _fancy,but I’m going to focus on a simple method that does a pretty good job and is easy to implement: frame averaging.

The concept is simple: rather than display new incoming keypoint values for each frame, take an average of values over n number of recent frames to help reduce noise in the data.

The video below shows the effect of averaging multiple frames on just the right wrist keypoint. The video starts with no smoothing and progresses to smoothing over 100 averaged frames.

At five and ten frames the keypoint is pretty smooth with little noticeable lag. As the number of averaged frames increases, the movement becomes smoother and the lag increases. By 50 frames the lag is very prominent. Depending on the type of interaction you are building the lag could be disruptive, but it could also be useful. (Lag typically encourages people to move slowly and deliberately.)

#tensorflowjs #posenet #programming #p5js #javascript

What is GEEK

Buddha Community

Simple Smoothing for PoseNet Keypoints
Gordon  Taylor

Gordon Taylor

1622886480

Simple Smoothing for PoseNet Keypoints

How to use frame averaging as a simple method to smooth keypoint data from PoseNet in p5.js.

Google’s Tensorflow.js  PoseNet model is extremely useful. It returns real-time pose estimation data in the browser from just a webcam, which allows for all sorts of accessible embodied interactions (like  novel navigation modes and  online dance parties) in the browser. Using the  ml5.js library, it’s also pretty easy to  get up and running.

The keypoint data returned from PoseNet is pretty noisy, so it’s helpful to add some smoothing to the points for better user experiences. There are many types of  smoothing algorithms, and they can be _very _fancy,but I’m going to focus on a simple method that does a pretty good job and is easy to implement: frame averaging.

The concept is simple: rather than display new incoming keypoint values for each frame, take an average of values over n number of recent frames to help reduce noise in the data.

The video below shows the effect of averaging multiple frames on just the right wrist keypoint. The video starts with no smoothing and progresses to smoothing over 100 averaged frames.

At five and ten frames the keypoint is pretty smooth with little noticeable lag. As the number of averaged frames increases, the movement becomes smoother and the lag increases. By 50 frames the lag is very prominent. Depending on the type of interaction you are building the lag could be disruptive, but it could also be useful. (Lag typically encourages people to move slowly and deliberately.)

#tensorflowjs #posenet #programming #p5js #javascript

SIMPLE BITCOIN TRADING STRATEGY | HOW TO FIND THE BEST TRADES

📺 The video in this post was made by Jayson Casper
The origin of the article: https://www.youtube.com/watch?v=SlFEwzrPKSk
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
(There is no limit to the amount of credit you can earn through referrals)
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #bitcoin trading #bitcoin trading strategy #simple bitcoin trading strategy #simple bitcoin trading strategy | how to find the best trades

Mia  Marquardt

Mia Marquardt

1622196300

Human Pose Estimation Using TensorFlow’s PoseNet Model

Tracking human movement with pose estimation using PoseNet

Is it possible to identify the movement/pose of a human body by doing an analysis of images and/or video? The answer to this question is YES.

This article will explain how to perform human pose estimation through a pre-trained model called PoseNet. Now, one-by-one, we will cover the topics mentioned below.

  1. What is human pose estimation?
  2. Types of pose estimation.
  3. Use of human pose estimation.
  4. Implementation.

#posenet #deep-learning #machine-learning #heartbeat #pose-estimation

Delbert  Ferry

Delbert Ferry

1620125743

In Database Machine Learning — Made Simple

One of the biggest problems with creating ML models is that the models are built in environments that are useless for deployment.

The fundamental issue is that Machine Learning deployment is a young and immature field, and it hasn’t yet developed the toolkits that database or software development have. Databases, for example, are widely available, stable, (sometimes) scalable and extremely fast. Because of this, we’re going to piggyback on the work that database engineers have done, and use their tools to our advantage. Here, we’ll focus on using scale-out RDBMS for model deployment.

#database #machine-learing #simple #data

Abdullah  Kozey

Abdullah Kozey

1620066840

Learn C by writing a simple game

taught myself about programming back in elementary school. My first programs were on the Apple II, but eventually, I learned C by reading books and practicing. And the best way to practice programming is to write sample programs that help exercise your new knowledge.

One program I like to write in a new language is a simple “guess the number” game. The computer picks a random number from 1 to 100, and you have to figure it out by making guesses. In another article, I showed how to write this “Guess the number” game in Bash, and my fellow Opensource.com authors have written articles about how to write it in JavaJulia, and other computer languages.

#c #game #simple