Tools for semi-automatic monitoring of industrial workflows

Authors:
Roland Mörzinger;Manolis Sardis;Igor Rosenberg;Helmut Grabner;Galina Veres;Imed Bouchrika;Marcus Thaler;Rene Schuster;Albert Hofmann;Georg Thallinger;Vassileios Anagnostopoulos;Dimitrios Kosmopoulos;Athanasios Voulodimos;Constantinos Lalos;Nikolaos Doulamis;Theodora Varvarigou;Rolando Palma Zelada;Ignacio Jubert Soler;Severin Stalder;Luc Van Gool;Lee Middleton;Zoheir Sabeur;Banafshe Arbab-Zavar;John Carter;Mark Nixon
Affiliations:
JOANNEUM RESEARCH, Graz, Austria;National Technical University of Athens, Athens, Greece;ATOS Origin, Barcelona, Spain;ETH, Zurich, Switzerland;University of Southampton, IT Innovation Centre, Southampton, United Kingdom;Southampton University, Southampton, United Kingdom;JOANNEUM RESEARCH, Graz, Austria;JOANNEUM RESEARCH, Graz, Austria;JOANNEUM RESEARCH, Graz, Austria;JOANNEUM RESEARCH, Graz, Austria;National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece;National Technical University of Athens, Athens, Greece;ATOS Origin, Barcelona, Spain;ATOS Origin, Barcelona, Spain;ETH, Zurich, Switzerland;ETH, Zurich, Switzerland;University of Southampton, IT Innovation Centre, Southampton, United Kingdom;University of Southampton, IT Innovation Centre, Southampton, United Kingdom;Southampton University, Southampton , United Kingdom;Southampton University, Southampton , United Kingdom;Southampton University, Southampton , United Kingdom
Venue:
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Year:
2010

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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.