The EgoHands dataset contains 48 Google Glass videos of complex, first-person interactions between two people. The main intention of this dataset is to enable better, data-driven approaches to understanding hands in first-person computer vision. The dataset offers * high quality, pixel-level segmentations of hands * the possibility to semantically distinguish between the observer’s hands and someone else’s hands, as well as left and right hands * virtually unconstrained hand poses as actors freely engage in a set of joint activities * lots of data with 15,053 ground-truth labeled hands Source: Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions
The HandNet dataset contains depth images of 10 participants' hands non-rigidly deforming in front of a RealSense RGB-D camera. The annotations are generated by a magnetic annotation technique. 6D pose is available for the center of the hand as well as the five fingertips (i.e. position and orientation of each). Source: Rule Of Thumb: Deep derotation for improved fingertip detection
The ObMan-Ego is a large-scale synthetic hand dataset with egocentric scenes in which the simulated hands are provided by ObMan. The dataset is used for a hand segmentation task and its sim-to-real adaptation benchmark. Training, validation, and testing sets contain 150, 000, 6, 500, and 6, 500 images, respectively.