Is that my hand? An egocentric dataset for hand disambiguation.

With the recent development of wearable cameras, the interest for research on the egocentric perspective is increasing. This opens the possibility to work on a specific object detection problem of hand detection and hand disambiguation. However, recent progress in egocentric hand disambiguation and even hand detection, especially using deep learning, has been limited by the lack of a large dataset, with suitable variations in subject, activity, and scene. In this paper, we propose a dataset that simulates daily activities, with variable illumination and people from different cultures and ethnicity to address daily life conditions. We increase the dataset size from previous works to allow robust solutions like deep neural networks that need a substantial amount of data for training. Our dataset consists of 50,000 annotated images with 10 different subjects doing 5 different daily activities (biking, eating, kitchen, office and running) in over 40 different scenes with variable illumination and changing backgrounds, and we compare with previous similar datasets.