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A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
| Content Provider | MDPI |
|---|---|
| Author | Chatzis, Theocharis Stergioulas, Andreas Konstantinidis, Dimitrios Dimitropoulos, Kosmas Daras, Petros |
| Copyright Year | 2020 |
| Description | The field of 3D hand pose estimation has been gaining a lot of attention recently, due to its significance in several applications that require human-computer interaction (HCI). The utilization of technological advances, such as cost-efficient depth cameras coupled with the explosive progress of Deep Neural Networks (DNNs), has led to a significant boost in the development of robust markerless 3D hand pose estimation methods. Nonetheless, finger occlusions and rapid motions still pose significant challenges to the accuracy of such methods. In this survey, we provide a comprehensive study of the most representative deep learning-based methods in literature and propose a new taxonomy heavily based on the input data modality, being RGB, depth, or multimodal information. Finally, we demonstrate results on the most popular RGB and depth-based datasets and discuss potential research directions in this rapidly growing field. |
| Starting Page | 6850 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10196850 |
| Journal | Applied Sciences |
| Issue Number | 19 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-30 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Computer Vision Deep Learning Neural Networks 3d Hand Pose Estimation |
| Content Type | Text |
| Resource Type | Article |
