Schoonbeek, Tim J.; Houben, Tim; Onvlee, Hans; de With, Peter H.N.; van der Sommen, Fons (2023): IndustReal Dataset of Egocentric Videos for Procedure Understanding. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/b008dd74-020d-4ea4-a8ba-7bb60769d224.v1

Dataset

x choose version: version 2 - 2024-08-23 (latest)

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

action recognition, artificial intelligence, assembly state detection, computer vision, multi-modal, object detection, procedure step recognition, procedure understanding, video recognition

Apache-2.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Tim J. Schoonbeek , Tim Houben , Hans Onvlee , Peter H.N. de With , Fons van der Sommen

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 GitHub for additional details and read-me files.

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.

• 2023-10-31 first online, published, posted

4TU.ResearchData

video frames (jpg), hand, pose and gaze tracking (csv), ground-truth labels (csv, json), object geometries (fbx). Model weights (.PYTH and .pt) and predictions (.csv) also provided. Everything is contained in zip or tar files.

https://github.com/TimSchoonbeek/IndustReal

Eindhoven University of Technology, Department of Electrical Engineering