This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop. In this challenge, we aim to estimate human poses from in-the-wild stitched panoramic images. Our method is built based on Faster R-CNN for human detection, and HRNet for human pose estimation. We describe technical details for the JRDB-Pose dataset, together with some experimental results. In the competition, we achieved 0.303 OSPAIOU and 64.047\% AP0.5 on the test set of JRDB-Pose.
@article{arxiv.2303.07141,
title = {An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop},
author = {Jiajun Fu and Yonghao Dang and Ruoqi Yin and Shaojie Zhang and Feng Zhou and Wending Zhao and Jianqin Yin},
journal= {arXiv preprint arXiv:2303.07141},
year = {2023}
}