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

Teaser image

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.

The HandSeg Dataset

The dataset comprises of 158,000 depth images captured with a RealSense SR300 camera and automatically annotated labels. The dataset contains 7 male and 3 female subjects. After the automatic labeling is done, the labels were carefully inspected to have no labeling errors. For more details, please see to the paper.

Note: An updated version of the dataset will be released soon with more images.

Dataset instructions

The dataset archive consists of images, masks folders