cff-version: 1.2.0
abstract: "<p>The multi-modal IndustReal dataset, accompanying our publication "IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting". Check out our <a href="https://github.com/TimSchoonbeek/IndustReal" target="_blank">GitHub</a> for additional details and read-me files.</p><p><br></p><p>Unlike currently available datasets, IndustReal contains procedural errors (such as omissions) as well as execution errors. A significant part of these errors are exclusively present in the validation and test sets, making IndustReal suitable to evaluate robustness of algorithms to new, unseen mistakes. Additionally, to encourage reproducibility and allow for scalable approaches trained on synthetic data, the 3D models of all parts are publicly available.</p>"
authors:
  - family-names: Schoonbeek
    given-names: Tim J.
  - family-names: Houben
    given-names: Tim
  - family-names: Onvlee
    given-names: Hans
  - family-names: de With
    given-names: Peter H.N.
  - family-names: van der Sommen
    given-names: Fons
title: "IndustReal Dataset of Egocentric Videos for Procedure Understanding"
keywords:
version: 2
identifiers:
  - type: doi
    value: 10.4121/b008dd74-020d-4ea4-a8ba-7bb60769d224.v2
license: Apache-2.0
date-released: 2024-08-23