HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images

TL;DR

This paper introduces HandSeg, a large-scale, automatically annotated dataset for depth-based hand segmentation, enabling improved training of algorithms to distinguish two hands, with publicly available data.

Contribution

The paper presents a novel automatic annotation method using RGBD sensors and colored gloves, creating a large, high-quality hand segmentation dataset that surpasses existing datasets.

Findings

Automatic annotations reduce labeling costs.

Existing datasets are insufficient for two-hand segmentation.

The dataset improves training for hand segmentation algorithms.

Abstract

We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset. Existing datasets are typically limited to a single hand. By exploiting the visual cues given by an RGBD sensor and a pair of colored gloves, we automatically generate dense annotations for two hand segmentation. This lowers the cost/complexity of creating high quality datasets, and makes it easy to expand the dataset in the future. We further show that existing datasets, even with data augmentation, are not sufficient to train a hand segmentation algorithm that can distinguish two hands. Source and datasets will be made publicly available.