Refined U-net: A new semantic technique on hand segmentation

Abstract

Hand segmentation aims to segment the hand profile however the biggest challenge is the segmentation hand over the face or skin-related environments. To solve these problems, many previous papers rely on a very deep neural network or collect new large-scale datasets on real-life scenes to increase the diversity and complexity. To perform it on a standard GPU, the training and inference time is still long, and always requires a large amount of GPU memory. In this paper, we propose a new hand segmentation technique, Refined U-Net, based on the original U-Net [1]. The main objective of Refined U-Net is to perform with few parameters and increasing the inference speed while achieving high accuracy during the hand segmentation process. We substantially improve its performance by refining the segmentation results and reducing the gap of feature vectors. In inference time, our Refined U-Net can prune the refinement block to increase the speed and reduce the number of parameters. We also eliminate the disadvantages of the popular dataset and propose a reliable way to create the virtual dataset. Our Refined U-Net can achieve 200 frames per second (FPS) on the GPU (RTX 2080Ti) and outperforms in accuracy for the state-of-the-art designs in Egohands [2] and GTEA [3] datasets.