Graduation Semester and Year
2021
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Vassilis Athitsos
Abstract
Pose estimation using Deep Neural Networks (DNNs) has shown outstanding performance in recent years, due to the availability of powerful GPUs and larger training datasets. However, there are still many challenges due to the large variability of human body appearances, lighting conditions, complex background, occlusions and postures. Among all these peculiarities, partial occlusions, and overlapping body poses often result in deviated pose predictions. These circumstances can result in wrong and sometimes unrealistic results. The human mind can predict such poses because of the underlying structural awareness of the geometry, of a human body. In this thesis, we discuss an efficient training technique that helps us to correct structurally implausible poses caused due to partial occlusions. We introduce a pose discriminator which helps us to incorporate priors about the human body's structure, into our model. As shown in the experiments, using this pose discriminator results in improved accuracy.
Keywords
Adversarial pose, Human pose estimation, Adversarial PoseNet, Structure aware, Generative adversarial, GAN, Adversarial 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
Sharma, Suryam, "STRUCTURE AWARE HUMAN POSE ESTIMATION USING ADVERSARIAL LEARNING" (2021). Computer Science and Engineering Theses. 450.
https://mavmatrix.uta.edu/cse_theses/450
Comments
Degree granted by The University of Texas at Arlington