Hand Pose Estimation: A Survey
It serves as a resource for researchers in computer vision by summarizing existing methods and datasets, but it is incremental as it does not introduce new techniques.
This paper surveys hand pose estimation, reviewing major approaches using depth maps or RGB images, and provides a comprehensive list of 22 datasets in the field.
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.