# Improved 2D Keypoint Detection in Out-of-Balance and Fall Situations -- combining input rotations and a kinematic model

arXiv: https://arxiv.org/abs/2112.12193 · HTML: https://mlsanity.com/paper/2112.12193/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Deep Dual Consecutive Network for Human Pose Estimation](https://mlsanity.com/paper/2103.07254/) (2021)
- [Motion Capture from Pan-Tilt Cameras with Unknown Orientation](https://mlsanity.com/paper/1908.11676/) (2019)

## Built on by (most cited first, 3 of 3)

- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [Visualizing Skiers' Trajectories in Monocular Videos](https://mlsanity.com/paper/2304.02994/) (2023)
- [Pose Estimation for Human Wearing Loose-Fitting Clothes: Obtaining Ground Truth Posture Using HFR Camera and Blinking LEDs](https://mlsanity.com/paper/2212.04820/) (2022)

## Abstract (arXiv)

Injury analysis may be one of the most beneficial applications of deep learning based human pose estimation. To facilitate further research on this topic, we provide an injury specific 2D dataset for alpine skiing, covering in total 533 images. We further propose a post processing routine, that combines rotational information with a simple kinematic model. We could improve detection results in fall situations by up to 21% regarding the PCK@0.2 metric.