Graduation Semester and Year
2020
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Vassilis Athitsos
Abstract
This paper addresses the problem of 3D hand pose annotations using a single depth camera. Although hand pose estimation methods rely critically on accurate 3D training data, creating such reliable training data is challenging and labor intensive. We propose a semi-automatic method for efficiently and accurately labeling the 3D hand key-points in a hand depth video. The process starts by selecting a subset of frames that are representative of all the frames in the dataset and the annotator only provides an estimate of the 2D hand key-points in these selected frames. We use this information to infer the 3D location of the joints for all the frames by enforcing appearance, temporal and distance constraints. Finally, we demonstrate that our method can generate 3D training data more accurately using less manual intervention and offering more flexibility in comparison to other state-of-the-art methods.
Keywords
Hand pose estimation, Data annotation, Computer vision, Machine learning
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Chris, Giffy Jerald, "SEMI-AUTOMATIC HAND POSE ESTIMATION USING A SINGLE DEPTH CAMERA" (2020). Computer Science and Engineering Theses. 424.
https://mavmatrix.uta.edu/cse_theses/424
Comments
Degree granted by The University of Texas at Arlington