Posenet is a real-time pose detection technique with which you can detect human poses in Image or Video. let's make a real-time project.

Pose estimation is a computer vision task for detecting the pose (i.e. It works by detecting a number of keypoints so that we can understand the main parts of the object and estimate its current orientation. Based on such keypoints, we will be able to form the shape of the object in either 2D or 3D. This tutorial covers how to build an Android app that estimates the human pose in standalone RGB images using the pretrained TFLite PoseNet model. The model predicts the locations of 17 keypoints of the human body, including the location of the eyes, nose, shoulders, etc.

During the quarantine, we're currently experiencing due to the COVID-19 pandemic our rights to move freely on the street are trimmed in favour of the common wellbeing. People can only go out in certain situations like doing the grocery. Many borders are closed and travelling is almosy totally banned in most countries. Virtual Walks is a project that uses Pose Estimation models along with LSTM neural networks in order to simulate walks in Google Street View. For pose estimation, PoseNet model has been adapted, while for the action detection part, an LSTM model has been developed using TensorFlow 2.0.

In recent years, the world has seen many major breakthroughs in the field of Machine Learning and so did the libraries used to write these programs. From NumPy to Tensorflow.js, each one faster and equipped with the best available technology of time than the previous one. One such library that we are going to talk about today is ml5.js. I recently started writing Deep Learning apps for browsers and can tell you this is pretty much fun and easy when you want to showcase your work to the general public or create an MVP in a hackathon with a cool dashboard and good-looking UI elements. I started programming on the web mainly because of the fact one can easily create an extensive user experience using just CSS and HTML5.