Event-based Egocentric Human Pose Estimation in Dynamic Environment
This addresses the problem of accurate human pose estimation in low-light or motion-blur conditions for applications like sports analysis and VR/AR, though it is incremental as it adapts existing methods to event-based cameras.
The paper tackles human pose estimation from a front-facing event-based camera in dynamic environments, introducing D-EventEgo, which uses a Motion Segmentation Module to remove dynamic objects and improve head pose estimation, resulting in outperforming the baseline in four out of five metrics on a synthetic dataset.
Estimating human pose using a front-facing egocentric camera is essential for applications such as sports motion analysis, VR/AR, and AI for wearable devices. However, many existing methods rely on RGB cameras and do not account for low-light environments or motion blur. Event-based cameras have the potential to address these challenges. In this work, we introduce a novel task of human pose estimation using a front-facing event-based camera mounted on the head and propose D-EventEgo, the first framework for this task. The proposed method first estimates the head poses, and then these are used as conditions to generate body poses. However, when estimating head poses, the presence of dynamic objects mixed with background events may reduce head pose estimation accuracy. Therefore, we introduce the Motion Segmentation Module to remove dynamic objects and extract background information. Extensive experiments on our synthetic event-based dataset derived from EgoBody, demonstrate that our approach outperforms our baseline in four out of five evaluation metrics in dynamic environments.