  Action recognition is essential for egocentric video understanding, allowing
automatic and continuous monitoring of Activities of Daily Living (ADLs)
without user effort. Existing literature focuses on 3D hand pose input, which
requires computationally intensive depth estimation networks or wearing an
uncomfortable depth sensor. In contrast, there has been insufficient research
in understanding 2D hand pose for egocentric action recognition, despite the
availability of user-friendly smart glasses in the market capable of capturing
a single RGB image. Our study aims to fill this research gap by exploring the
field of 2D hand pose estimation for egocentric action recognition, making two
contributions. Firstly, we introduce two novel approaches for 2D hand pose
estimation, namely EffHandNet for single-hand estimation and EffHandEgoNet,
tailored for an egocentric perspective, capturing interactions between hands
and objects. Both methods outperform state-of-the-art models on H2O and FPHA
public benchmarks. Secondly, we present a robust action recognition
architecture from 2D hand and object poses. This method incorporates
EffHandEgoNet, and a transformer-based action recognition method. Evaluated on
H2O and FPHA datasets, our architecture has a faster inference time and
achieves an accuracy of 91.32% and 94.43%, respectively, surpassing state of
the art, including 3D-based methods. Our work demonstrates that using 2D
skeletal data is a robust approach for egocentric action understanding.
Extensive evaluation and ablation studies show the impact of the hand pose
estimation approach, and how each input affects the overall performance.
