Active Human Pose Estimation via an Autonomous UAV Agent (2407.01811v1)

Abstract: 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.

• The paper presents an integrated framework combining NeRF-generated datasets, PoseErrNet error estimation, and a P-ESDF-based motion planner.
• The system dynamically adapts UAV trajectories to overcome occlusions and secure optimal viewpoints for human pose estimation.
• Experimental results show enhanced accuracy using metrics like PCK and MSE compared to static viewpoint methods.

Active Human Pose Estimation via an Autonomous UAV Agent

The paper "Active Human Pose Estimation via an Autonomous UAV Agent," authored by Jingxi Chen, Botao He, Chahat Deep Singh, Cornelia Fermüller, and Yiannis Aloimonos from the Perception and Robotics Group at the University of Maryland, presents an innovative approach to tackling the challenges of human pose estimation using autonomous Unmanned Aerial Vehicles (UAVs). The core focus lies in addressing the occlusions often encountered in dynamic human activities by leveraging UAVs to reposition the camera for optimal viewpoint acquisition. The authors introduce a multi-component system to overcome these challenges, formalizing the process and demonstrating its efficacy through both simulated and real-world experiments.

Key Components

The proposed approach integrates the following key components:

1. NeRF-based Drone-View Data Generation Framework
2. On-Drone Network for Camera View Error Estimation (PoseErrNet)
3. Combined Planner for Motion Planning

NeRF-based Drone-View Data Generation Framework

The authors utilize the Neural Radiance Field (NeRF) technique, specifically the HumanNeRF method, to generate extensive drone-view datasets of human activities. This framework synthesizes images from various camera angles and human poses, accurately reflecting real-world scenarios. Such data serves as the training foundation for the PoseErrNet, facilitating the UAV’s understanding of human pose dynamics under different viewing conditions.

On-Drone Network for Camera View Error Estimation (PoseErrNet)

PoseErrNet is designed to predict human pose estimation errors for various viewing angles. Input images are processed to perform initial 2D human pose estimation, which feeds into PoseErrNet. This network predicts a 3D perception guidance field, suggesting the most promising next viewing angles. The authors implement an input normalization process to handle input variations, enhancing PoseErrNet's robustness against translation, rotation, and scaling deviations in the observed keypoints.

Combined Planner for Motion Planning

The combined planner merges traditional navigation constraints with perceptual guidance derived from PoseErrNet. Utilizing a differentiable Pose-enhanced Euclidean Distance Field (P-ESDF), the planner integrates perception loss into the UAV’s motion planning cost function. This integration ensures that the UAV can execute smooth trajectories, avoid obstacles, and maintain optimal viewpoints for precise human pose estimation.

Experimental Results

The authors validate their system through various simulated environments and real-world experiments, demonstrating its capability to maintain optimal viewpoints dynamically. The UAV system is shown to adaptively shift between the best and the second-best viewpoints to ensure visibility and avoid collisions. Performance metrics such as Percentage of Correct Keypoints (PCK) and Mean Squared Error (MSE) are utilized to evaluate the system's accuracy, highlighting improvements over traditional static viewpoint methods.

Implications and Future Developments

The proposed system has significant implications for practical applications in aerial cinematography, surveillance, and other domains requiring dynamic human activity monitoring. By enhancing the UAV's ability to autonomously identify and adjust to optimal viewpoints, the method improves the quality and reliability of human pose estimation. The adaptability and safety mechanisms embedded within the system ensure its operational feasibility across varied and complex environments.

Future developments in AI could further refine the system, leveraging advancements in machine learning algorithms and sensor technologies. Enhanced training data diversity and real-time processing capabilities could push the limits of UAV-based human pose estimation, driving innovation across multiple industries.

In summary, the paper establishes a robust framework for active human pose estimation via autonomous UAVs, blending sophisticated data generation, perceptual error estimation, and motion planning techniques. This integrated approach answers both theoretical and practical challenges, positioning itself as a foundational reference for future research in autonomous UAV applications in dynamic settings.