# Human Pose Estimation using Motion Priors and Ensemble Models

## TL;DR

This paper introduces two novel methodologies for human pose estimation: 3D pose tracking with motion priors and 2D pose estimation using ensemble models, enhancing accuracy for activity recognition tasks.

## Contribution

It proposes new approaches combining motion priors and ensemble modeling to improve human pose estimation accuracy in images and videos.

## Key findings

- Improved 3D pose tracking accuracy with motion priors.
- Enhanced 2D pose estimation robustness through ensemble models.
- Applicable to various human activity recognition applications.

## Abstract

Human pose estimation in images and videos is one of key technologies for realizing a variety of human activity recognition tasks (e.g., human-computer interaction, gesture recognition, surveillance, and video summarization). This paper presents two types of human pose estimation methodologies; 1) 3D human pose tracking using motion priors and 2) 2D human pose estimation with ensemble modeling.