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Wearable cameras allow to acquire images and videos from the user'sperspective. These data can be processed to understand humans behavior. Despitehuman behavior analysis has been thoroughly investigated in third personvision, it is still understudied in egocentric settings and in particular inindustrial scenarios. To encourage research in this field, we present MECCANO,a multimodal dataset of egocentric videos to study humans behaviorunderstanding in industrial-like settings. The multimodality is characterizedby the presence of gaze signals, depth maps and RGB videos acquiredsimultaneously with a custom headset. The dataset has been explicitly labeledfor fundamental tasks in the context of human behavior understanding from afirst person view, such as recognizing and anticipating human-objectinteractions. With the MECCANO dataset, we explored five different tasksincluding 1) Action Recognition, 2) Active Objects Detection and Recognition,3) Egocentric Human-Objects Interaction Detection, 4) Action Anticipation and5) Next-Active Objects Detection. We propose a benchmark aimed to study humanbehavior in the considered industrial-like scenario which demonstrates that theinvestigated tasks and the considered scenario are challenging forstate-of-the-art algorithms. To support research in this field, we publicyrelease the dataset at https://iplab.dmi.unict.it/MECCANO/.

MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain