ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios

ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and equipments (e.g., oscilloscope). The 51 egocentric video sequences are densely annotat...

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Published in:arXiv.org 2023-11
Main Authors: Ragusa, Francesco, Leonardi, Rosario, Mazzamuto, Michele, Bonanno, Claudia, Scavo, Rosario, Furnari, Antonino, Farinella, Giovanni Maria
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Language:English
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Benchmarks
Datasets
Screwdrivers
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creator Ragusa, Francesco
Leonardi, Rosario
Mazzamuto, Michele
Bonanno, Claudia
Scavo, Rosario
Furnari, Antonino
Farinella, Giovanni Maria
description ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and equipments (e.g., oscilloscope). The 51 egocentric video sequences are densely annotated with a rich set of labels that enable the systematic study of human behavior in the industrial domain. We provide benchmarks on four tasks related to human behavior: 1) untrimmed temporal detection of human-object interactions, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human behavior in industrial scenarios. We publicly release the dataset at https://iplab.dmi.unict.it/ENIGMA-51.
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subjects Benchmarks
Datasets
Screwdrivers
title ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios
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