Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities

Sener, Fadime; Chatterjee, Dibyadip; Shelepov, Daniel; He, Kai; Singhania, Dipika; Wang, Robert; Yao, Angela

doi:10.48550/arxiv.2203.14712

Cited by 1 publication

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“…determining interactions in kitchens [13,14] and home situations [15]. Recently, Ragusa et al [16] and Sener et al [17] introduced egocentric video datasets of human-object interactions in industrial-like settings. Recognizing the interactions between objects and humans can provide valuable information when trying to understand what a human is doing, but does not provide conformation of whether a task is actually completed.…”

Section: Related Workmentioning

confidence: 99%

Augmented Reality for Automatically Generating Robust Manufacturing and Maintenance Logs

Schoonbeek¹,

Frisco²,

Onvlee³

et al. 2022

lim

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Logs describing the execution of procedural steps during manufacturing and maintenance tasks are important for quality control and configuration management. Such logs are currently hand-written or typed during a procedure, which requires engineers to frequently step away from their work and results in difficulties for searching and optimizing logs. In this paper, we propose to automatically generate standardized, searchable logs, by visually perceiving and monitoring the progress of the procedure in real-time, and comparing this to the expected procedure. Unlike related work, we propose an approach which does not restrict the engineers to rigid, sequential sequences and instead allows them to execute procedures in a variety of different sequences where possible. The proposed framework is experimentally validated on the task of (dis)assembling a Duplo block model and operates properly when occlusions are absent.

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