ENIGMA-51: Towards a Fine-Grained Understanding of Human Behavior in Industrial Scenarios
Abstract
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.
Cite
Text
Ragusa et al. "ENIGMA-51: Towards a Fine-Grained Understanding of Human Behavior in Industrial Scenarios." Winter Conference on Applications of Computer Vision, 2024.Markdown
[Ragusa et al. "ENIGMA-51: Towards a Fine-Grained Understanding of Human Behavior in Industrial Scenarios." Winter Conference on Applications of Computer Vision, 2024.](https://mlanthology.org/wacv/2024/ragusa2024wacv-enigma51/)BibTeX
@inproceedings{ragusa2024wacv-enigma51,
title = {{ENIGMA-51: Towards a Fine-Grained Understanding of Human Behavior in Industrial Scenarios}},
author = {Ragusa, Francesco and Leonardi, Rosario and Mazzamuto, Michele and Bonanno, Claudia and Scavo, Rosario and Furnari, Antonino and Farinella, Giovanni Maria},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2024},
pages = {4549-4559},
url = {https://mlanthology.org/wacv/2024/ragusa2024wacv-enigma51/}
}