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Tools for semi-automatic monitoring of industrial workflows

ARTEMIS '10: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams

Pages 81 - 86

https://doi.org/10.1145/1877868.1877889

Published: 29 October 2010 Publication History

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Abstract

This paper describes a tool chain for monitoring complex workflows. Statistics obtained from automatic workflow monitoring in a car assembly environment assist in improving industrial safety and process quality. To this end, we propose automatic detection and tracking of humans and their activity in multiple networked cameras. The described tools offer human operators retrospective analysis of a huge amount of pre-recorded and analyzed footage from multiple cameras in order to get a comprehensive overview of the workflows. Furthermore, the tools help technical administrators in adjusting algorithms by letting the user correct detections (for relevance feedback) and ground truth for evaluation. Another important feature of the tool chain is the capability to inform the employees about potentially risky conditions using the tool for automatic detection of unusual scenes.

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Cited By

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Mannhardt FBovo ROliveira MJulier S(2018)A Taxonomy for Combining Activity Recognition and Process Discovery in Industrial EnvironmentsIntelligent Data Engineering and Automated Learning – IDEAL 201810.1007/978-3-030-03496-2_10(84-93)Online publication date: 9-Nov-2018
https://doi.org/10.1007/978-3-030-03496-2_10
Lalos CVoulodimos ADoulamis AVarvarigou T(2014)Efficient tracking using a robust motion estimation techniqueMultimedia Tools and Applications10.1007/s11042-012-0994-369:2(277-292)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s11042-012-0994-3

Index Terms

Tools for semi-automatic monitoring of industrial workflows
1. Computing methodologies
  1. Machine learning

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ARTEMIS '10: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams

October 2010

104 pages

ISBN:9781450301633

DOI:10.1145/1877868

General Chairs:
Anastasios Doulamis
Technical University of Crete, Greece
,
Jordi Gonzàlez
Universitat Autònoma de Barcelona - CVC, Spain
,
Program Chairs:
Marco Bertini
University of Firenze, Italy
,
Luc van Gool
Technische Hochschule Zürich (ETHz), Switzerland
,
Nikolaos Matsatsinis
Technical University of Crete, Greece
,
Thomas B. Moeslund
University of Aalborg, Denmark
,
Mark Nixon
University of Southampton, United Kingdom
,
Liang Wang
University of Bath, United Kingdom

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2010

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October 29, 2010

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Mannhardt FBovo ROliveira MJulier S(2018)A Taxonomy for Combining Activity Recognition and Process Discovery in Industrial EnvironmentsIntelligent Data Engineering and Automated Learning – IDEAL 201810.1007/978-3-030-03496-2_10(84-93)Online publication date: 9-Nov-2018
https://doi.org/10.1007/978-3-030-03496-2_10
Lalos CVoulodimos ADoulamis AVarvarigou T(2014)Efficient tracking using a robust motion estimation techniqueMultimedia Tools and Applications10.1007/s11042-012-0994-369:2(277-292)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s11042-012-0994-3

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Monitoring of Grid scientific workflows

Improved person detection in industrial environments using multiple self-calibrated cameras

A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments

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