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

Understand

Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications.

• However, existing datasets are far from sufficient 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 Ego2Hands, a large-scale RGB-based egocentric hand segmentation/detection dataset that is semi-automatically annotated and a color-invariant compositing-based data generation technique capable of creating training data with large quantity and variety.