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ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by19 subjects who followed instructions to complete the repair of electricalboards using industrial tools (e.g., electric screwdriver) and equipments(e.g., oscilloscope). The 51 egocentric video sequences are densely annotatedwith a rich set of labels that enable the systematic study of human behavior inthe industrial domain. We provide benchmarks on four tasks related to humanbehavior: 1) untrimmed temporal detection of human-object interactions, 2)egocentric human-object interaction detection, 3) short-term object interactionanticipation and 4) natural language understanding of intents and entities.Baseline results show that the ENIGMA-51 dataset poses a challenging benchmarkto study human behavior in industrial scenarios. We publicly release thedataset at https://iplab.dmi.unict.it/ENIGMA-51.

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