TY - GEN
T1 - Handseg: An automatically labeled dataset for hand segmentation from depth images
AU - Bojja, Abhishake Kumar
AU - Mueller, Franziska
AU - Malireddi, Sri Raghu
AU - Oberweger, Markus
AU - Lepetit, Vincent
AU - Theobalt, Christian
AU - Yi, Kwang Moo
AU - Tagliasacchi, Andrea
PY - 2019
Y1 - 2019
N2 - 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 are publicly available at the project page
AB - 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 are publicly available at the project page
M3 - Conference paper
SP - 151
EP - 158
BT - 2019 16th Conference on Computer and Robot Vision (CRV)
ER -