Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection
- URL: http://arxiv.org/abs/2011.07252v4
- Date: Mon, 20 Dec 2021 10:37:48 GMT
- Title: Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and Detection
- Authors: Fanqing Lin, Brian Price, Tony Martinez
- Abstract summary: We present Ego2Hands, a large-scale RGB-based egocentric hand segmentation/detection dataset that is semi-automatically annotated.
For quantitative analysis, we manually annotated an evaluation set that significantly exceeds existing benchmarks in quantity, diversity and annotation accuracy.
- Score: 1.0742675209112622
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. For quantitative analysis, we manually
annotated an evaluation set that significantly exceeds existing benchmarks in
quantity, diversity and annotation accuracy. We provide cross-dataset
evaluation as well as thorough analysis on the performance of state-of-the-art
models on Ego2Hands to show that our dataset and data generation technique can
produce models that generalize to unseen environments without domain
adaptation.
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