Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection

Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications. However, existing datasets are far from sufficient both in terms of size and variety due to the infeasibility of manual annotation of large amounts of segmentation and detection data. As a result, current methods are limited by many underlying assumptions such as constrained environment, consistent skin color and lighting. In this work, we present a large-scale RGB-based egocentric hand segmentation/detection dataset Ego2Hands that is automatically annotated and a color-invariant compositing-based data generation technique capable of creating unlimited training data with variety. For quantitative analysis, we manually annotated an evaluation set that significantly exceeds existing benchmarks in quantity, diversity and annotation accuracy. We show that our dataset and training technique can produce models that generalize to unseen environments without domain adaptation. We introduce Convolutional Segmentation Machine (CSM) as an architecture that better balances accuracy, size and speed and provide thorough analysis on the performance of state-of-the-art models on the Ego2Hands dataset.