%0 Generic
%A Schoonbeek, Tim J.
%A Houben, Tim
%A Onvlee, Hans
%A de With, Peter H.N.
%A van der Sommen, Fons
%D 2023
%T IndustReal Dataset of Egocentric Videos for Procedure Understanding
%U 
%R 10.4121/b008dd74-020d-4ea4-a8ba-7bb60769d224.v1
%K computer vision
%K artificial intelligence
%K video recognition
%K multi-modal
%K procedure understanding
%K procedure step recognition
%K action recognition
%K object detection
%K assembly state detection
%X <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>
%I 4TU.ResearchData