One of the intuitive instruction methods in robot navigation is a pointing
gesture. In this study, we propose a method using an omnidirectional camera to
eliminate the user/object position constraint and the left/right constraint of
the pointing arm. Although the accuracy of skeleton and object detection is low
due to the high distortion of equirectangular images, the proposed method
enables highly accurate estimation by repeatedly extracting regions of interest
from the equirectangular image and projecting them onto perspective images.
Furthermore, we found that training the likelihood of the target object in
machine learning further improves the estimation accuracy.

使用全景相机提出了一种使用指向手势的机器人导航直观操作方式，通过反复从全景图像中提取感兴趣区域并投影到透视图像上，消除了用户 / 目标位置约束和指向臂的左 / 右约束，并通过机器学习训练目标对象的可能性进一步提高了估计的准确性。

无死角：从全景图像中定向目标估计

Point Anywhere: Directed Object Estimation from Omnidirectional Images

Human pose estimation (HPE) with convolutional neural networks (CNNs) for
indoor monitoring is one of the major challenges in computer vision. In
contrast to HPE in perspective views, an indoor monitoring system can consist
of an omnidirectional camera with a field of view of 180{\deg} to detect the
pose of a person with only one sensor per room. To recognize human pose, the
detection of keypoints is an essential upstream step. In our work we propose a
new dataset for training and evaluation of CNNs for the task of keypoint
detection in omnidirectional images. The training dataset, THEODORE+, consists
of 50,000 images and is created by a 3D rendering engine, where humans are
randomly walking through an indoor environment. In a dynamically created 3D
scene, persons move randomly with simultaneously moving omnidirectional camera
to generate synthetic RGB images and 2D and 3D ground truth. For evaluation
purposes, the real-world PoseFES dataset with two scenarios and 701 frames with
up to eight persons per scene was captured and annotated. We propose four
training paradigms to finetune or re-train two top-down models in MMPose and
two bottom-up models in CenterNet on THEODORE+. Beside a qualitative evaluation
we report quantitative results. Compared to a COCO pretrained baseline, we
achieve significant improvements especially for top-view scenes on the PoseFES
dataset. Our datasets can be found at
this https URL

该研究提出了一个新的数据集（THEODORE+），用于训练和评估卷积神经网络（CNNs）在全景相机中识别人体姿态，以及针对 THEODORE + 数据集的四种重新训练或微调神经网络的训练方法，并在 PoseFES 数据集中取得较明显的改进。