Source-Independent Domain Adaptive Human Pose Detection

EasyChair Preprint 14876

Abstract

Human pose estimation is a fundamental challenge in computer vision, crucial for applications ranging from human-computer interaction to sports analytics and medical diagnostics. Traditional domain adaptation methods for pose estimation rely heavily on labeled source domain data, which often limits their effectiveness due to issues such as data privacy and the inability to adapt to new domains. This article introduces a novel approach to source-independent domain adaptive human pose detection, focusing on leveraging target domain data without relying on source domain data during adaptation. The proposed method addresses the challenges associated with distribution shifts and provides robust pose detection performance across diverse environments. By utilizing unsupervised learning techniques and domain alignment strategies, this framework achieves high accuracy in pose detection without the need for source domain labels. The article discusses the implementation process, key challenges, and potential applications of this approach, offering new insights into the capabilities of source-independent domain adaptation in human pose estimation.

Keyphrases: Data privacy in machine learning, Distribution Shifts, Source-independent domain adaptation, Target domain adaptation, computer vision, cross-domain learning, domain alignment, human pose estimation, pose detection, unsupervised learning

@booklet{EasyChair:14876,
  author    = {Toluwani Bolu},
  title     = {Source-Independent Domain Adaptive Human Pose Detection},
  howpublished = {EasyChair Preprint 14876},
  year      = {EasyChair, 2024}}