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 $\text{OSPA}_{\text{IOU}}$ and 64.047\% $\text{AP}_{\text{0.5}}$ on the test set of JRDB-Pose.

翻译：本技术报告描述了我们在ECCV 2022人类环境导航视觉感知研讨会姿态估计挑战中获得第一名解决方案。该挑战的目标是从自然场景拼接全景图像中估计人体姿态。我们的方法基于Faster R-CNN进行人体检测，并采用HRNet进行人体姿态估计。我们介绍了JRDB-Pose数据集的技术细节以及部分实验结果。在竞赛中，我们在JRDB-Pose测试集上取得了0.303 $\text{OSPA}_{\text{IOU}}$和64.047% $\text{AP}_{\text{0.5}}$的成绩。