  Wearable cameras allow to collect images and videos of humans interacting
with the world. While human-object interactions have been thoroughly
investigated in third person vision, the problem has been understudied in
egocentric settings and in industrial scenarios. To fill this gap, we introduce
MECCANO, the first dataset of egocentric videos to study human-object
interactions in industrial-like settings. MECCANO has been acquired by 20
participants who were asked to build a motorbike model, for which they had to
interact with tiny objects and tools. The dataset has been explicitly labeled
for the task of recognizing human-object interactions from an egocentric
perspective. Specifically, each interaction has been labeled both temporally
(with action segments) and spatially (with active object bounding boxes). With
the proposed dataset, we investigate four different tasks including 1) action
recognition, 2) active object detection, 3) active object recognition and 4)
egocentric human-object interaction detection, which is a revisited version of
the standard human-object interaction detection task. Baseline results show
that the MECCANO dataset is a challenging benchmark to study egocentric
human-object interactions in industrial-like scenarios. We publicy release the
dataset at https://iplab.dmi.unict.it/MECCANO.
