Automatic workflow monitoring in industrial environments

Authors:
Galina Veres;Helmut Grabner;Lee Middleton;Luc Van Gool
Affiliations:
University of Southampton, IT Innovation Centre, UK;Computer Vision Laboratory, ETH Zurich, Switzerland;University of Southampton, IT Innovation Centre, UK;Computer Vision Laboratory, ETH Zurich, Switzerland and ESAT, PSI and IBBT, K.U. Leuven, Belgium
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Year:
2010

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Abstract

Robust automatic workflow monitoring using visual sensors in industrial environments is still an unsolved problem. This is mainly due to the difficulties of recording data in work settings and the environmental conditions (large occlusions, similar background/foreground) which do not allow object detection/tracking algorithms to perform robustly. Hence approaches analysing trajectories are limited in such environments. However, workflow monitoring is especially needed due to quality and safety requirements. In this paper we propose a robust approach for workflow classification in industrial environments. The proposed approach consists of a robust scene descriptor and an efficient time series analysis method. Experimental results on a challenging car manufacturing dataset showed that the proposed scene descriptor is able to detect both human and machinery related motion robustly and the used time series analysis method can classify tasks in a given workflow automatically.