The Human Pose Estimation is the task of using a machine learning model to estimate the approximate pose of a person from an image or a video by estimating the spatial locations of key body joints that is called keypoints.
- There are total 17 keypoints that are used by algorithm to estimate the pose of human body.
- This step is a crucial prerequisite to multiple tasks of computer vision which include human action recognition, human tracking, human-computer interaction and video surveillance.
- It can be used to estimate either a single pose or multiple poses, meaning there is a version of the algorithm that can detect only one person in an image/video and one version that can detect multiple persons in an image/video.
- The aim is to deliver the basic use cases of the Pose Net model for real-time human pose estimation using a webcam feed as the data. Now, the challenge is to create an advanced webcam filter that has detection functionalities like the Snapchat camera.
