Egocentric Hand-object Interaction Detection

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, handobject masks to jointly determine the interaction status. In order to solve the problem of hand pose estimation due to inhand 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.[1] on selected images from EPIC-KITCHENS [2] 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 30 FPS which is much more efficient than Shan's (1 ∼ 2 FPS). A demo can be found from https://www.youtube.com/watch?v=XVj3zBuynmQ.