One of the core activities of an active observer involves moving to secure a
"better" view of the scene, where the definition of "better" is task-dependent.
This paper focuses on the task of human pose estimation from videos capturing a
person's activity. Self-occlusions within the scene can complicate or even
prevent accurate human pose estimation. To address this, relocating the camera
to a new vantage point is necessary to clarify the view, thereby improving 2D
human pose estimation. This paper formalizes the process of achieving an
improved viewpoint. Our proposed solution to this challenge comprises three
main components: a NeRF-based Drone-View Data Generation Framework, an On-Drone
Network for Camera View Error Estimation, and a Combined Planner for devising a
feasible motion plan to reposition the camera based on the predicted errors for
camera views. The Data Generation Framework utilizes NeRF-based methods to
generate a comprehensive dataset of human poses and activities, enhancing the
drone's adaptability in various scenarios. The Camera View Error Estimation
Network is designed to evaluate the current human pose and identify the most
promising next viewing angles for the drone, ensuring a reliable and precise
pose estimation from those angles. Finally, the combined planner incorporates
these angles while considering the drone's physical and environmental
limitations, employing efficient algorithms to navigate safe and effective
flight paths. This system represents a significant advancement in active 2D
human pose estimation for an autonomous UAV agent, offering substantial
potential for applications in aerial cinematography by improving the
performance of autonomous human pose estimation and maintaining the operational
safety and efficiency of UAVs.

通过使用 NeRF 方法和无人机，本文提出了一个新的系统，利用相机的重新定位来改善 2D 人体姿态估计，从而提高无人机的性能并保持操作安全和效率。

通过自主无人机代理进行主动人体姿势估计

Active Human Pose Estimation via an Autonomous UAV Agent

In this work, we present a novel framework for camera relocation in
autonomous vehicles, leveraging deep neural networks (DNN). While existing
literature offers various DNN-based camera relocation methods, their deployment
is hindered by their high computational demands during inference. In contrast,
our approach addresses this challenge through edge cloud collaboration.
Specifically, we strategically offload certain modules of the neural network to
the server and evaluate the inference time of data frames under different
network segmentation schemes to guide our offloading decisions. Our findings
highlight the vital role of server-side offloading in DNN-based camera
relocation for autonomous vehicles, and we also discuss the results of data
fusion. Finally, we validate the effectiveness of our proposed framework
through experimental evaluation.

我们提出了一个新的框架，用于自动驾驶汽车中的相机重定位，利用深度神经网络 (DNN)。通过边缘云协作，我们解决了现有方法在推理期间高计算需求的问题，通过将神经网络的某些模块策略性地卸载到服务器，并评估数据帧在不同网络分割方案下的推理时间，以指导我们的卸载决策。我们的研究结果突出了服务器端卸载在基于 DNN 的自动驾驶汽车相机重定位中的重要作用，我们还讨论了数据融合的结果。最后，通过实验评估验证了我们提出的框架的有效性。