  We consider the problem of detecting Egocentric HumanObject Interactions
(EHOIs) in industrial contexts. Since collecting and labeling large amounts of
real images is challenging, we propose a pipeline and a tool to generate
photo-realistic synthetic First Person Vision (FPV) images automatically
labeled for EHOI detection in a specific industrial scenario. To tackle the
problem of EHOI detection, we propose a method that detects the hands, the
objects in the scene, and determines which objects are currently involved in an
interaction. We compare the performance of our method with a set of
state-of-the-art baselines. Results show that using a synthetic dataset
improves the performance of an EHOI detection system, especially when few real
data are available. To encourage research on this topic, we publicly release
the proposed dataset at the following url:
https://iplab.dmi.unict.it/EHOI_SYNTH/.
