This dataset is the output of images processed by OpenPose. OpenPose is a pose estimation software that identifies the location of key body parts and joints from images or video frames and provides a high-level understanding of the human pose through a key point delimitated outline of the body that implies the underlying pose. The dataset consists of multiple entries which report on the key points of various pose images collected from the public domain - a small sample set images is provided that depicts the OpenPose mapping interpreability of the data. The dataset consists of 50,727 entries:

37,748 entries are associated with a 'stand' pose classification 12,979 entries are associated with a 'sit' pose classification

These key points are reported in csv format (stored in an Excel book) with the following data attributes:

Original pose image file name List of 18 normalised XY coordinates for each body part/joint, and the associated confidence score of its localisation as reported by OpenPose. The pose class (either sit or stand)

This datset served in the preliminary experiments as part of the Master's dissertation "Novel data augmentation schemes for pose classification using a convolutional neural network" to faciliate a binary pose classification problem. Author: JS du Toit

The Hand Key Point Skeleton Dataset is designed for applications in visual entertainment and augmented/virtual reality (AR/VR), featuring a collection of indoor-collected images with a high resolution of 3024 x 4032 pixels. This dataset focuses on labeling 21 key points of the hand skeleton, capturing specific single-handed or two-handed poses such as forming a heart shape, placing a hand on the cheek, stretching, and more.