approved
PREGO: Online mistake detection in PRocedural EGOcentric videos

Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability has a wide range of applications across various fields, such as manufacturing and healthcare. The nature of procedural mistakes is open-set since novel types of failures might occur, which calls for one-class classifiers trained on correctly executed procedures. However, no technique can currently detect open-set procedural mistakes online. We propose PREGO, the first online one-class classification model for mistake detection in PRocedural EGOcentric videos. PREGO is based on an online action recognition component to model the current action, and a symbolic reasoning module to predict the next actions. Mistake detection is performed by comparing the recognized current action with the expected future one. We evaluate PREGO on two procedural egocentric video datasets, Assembly101 and Epic-tent, which we adapt for online benchmarking of procedural mistake detection to establish suitable benchmarks, thus defining the Assembly101-O and Epic-tent-O datasets, respectively. The code is available online.

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Additional Info
Field Value
Accessibility OnLine
AccessibilityMode Download
Associate Project FAIR
Basic rights Download
CreationDate 2025-03-24
Creator Farinella, Giovanni, giovanni.farinella@unict.it, orcid.org/0000-0002-6034-0432
Field/Scope of use Any use
Group Others
Owner Farinella, Giovanni, giovanni.farinella@unict.it, orcid.org/0000-0002-6034-0432
Programming Language Python
SoBigData Node SoBigData IT
Sublicense rights No
Territory of use World Wide
Thematic Cluster Other
system:type Method
Management Info
Field Value
Author Farinella Giovanni Maria
Maintainer Farinella Giovanni Maria
Version 1
Last Updated 22 June 2025, 01:09 (CEST)
Created 22 June 2025, 01:09 (CEST)