Real-time hand detection using continuous skeletons

Abstract

In this paper, a fast and reliable method for hand detection based on continuous skeletons approach is presented. It demonstrates real-time working speed and high detection accuracy (3–5% both FAR and FRR) on a large dataset (50 persons, 80 videos, 2322 frames). These make it suitable for use as a part of modern hand identification systems including mobile ones. Overall, the study shows that continuous skeletons approach can be used as prior for object and background color models in segmentation methods with supervised learning (e.g., interactive segmentation with seeds or abounding box).