Hand Pose Estimation: A Survey

TL;DR

This survey reviews recent advances in hand pose estimation using depth and RGB images, covering major approaches, datasets, and their strengths and weaknesses in the context of deep learning advancements.

Contribution

It provides the most comprehensive list of hand pose estimation datasets and compares different methods and their effectiveness.

Findings

Deep learning has significantly advanced hand pose estimation.

A wide variety of datasets exist, with detailed properties listed.

Different approaches have distinct strengths and weaknesses.

Abstract

The success of Deep Convolutional Neural Networks (CNNs) in recent years in almost all the Computer Vision tasks on one hand, and the popularity of low-cost consumer depth cameras on the other, has made Hand Pose Estimation a hot topic in computer vision field. In this report, we will first explain the hand pose estimation problem and will review major approaches solving this problem, especially the two different problems of using depth maps or RGB images. We will survey the most important papers in each field and will discuss the strengths and weaknesses of each. Finally, we will explain the biggest datasets in this field in detail and list 22 datasets with all their properties. To the best of our knowledge this is the most complete list of all the datasets in the hand pose estimation field.