Egocentric Hand-object Interaction Detection

• URL: http://arxiv.org/abs/2211.09067v1
• Date: Wed, 16 Nov 2022 17:31:40 GMT
• Title: Egocentric Hand-object Interaction Detection
• Authors: Yao Lu, Yanan Liu
• Abstract summary: We use a multi-cam system to capture hand pose data from multiple perspectives. Our method can run over $textbf30$ FPS which is much more efficient than Shan's.
• Score: 13.639883596251313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a method to jointly determine the status of hand-object interaction. This is crucial for egocentric human activity understanding and interaction. From a computer vision perspective, we believe that determining whether a hand is interacting with an object depends on whether there is an interactive hand pose and whether the hand is touching the object. Thus, we extract the hand pose, hand-object masks to jointly determine the interaction status. In order to solve the problem of hand pose estimation due to in-hand object occlusion, we use a multi-cam system to capture hand pose data from multiple perspectives. We evaluate and compare our method with the most recent work from Shan et al. \cite{Shan20} on selected images from EPIC-KITCHENS \cite{damen2018scaling} dataset and achieve $89\%$ accuracy on HOI (hand-object interaction) detection which is comparative to Shan's ($92\%$). However, for real-time performance, our method can run over $\textbf{30}$ FPS which is much more efficient than Shan's ($\textbf{1}\sim\textbf{2}$ FPS). A demo can be found from https://www.youtube.com/watch?v=XVj3zBuynmQ

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