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A Semi-Supervised Approach for Industrial Workflow Recognition

Authors:
Eftychios Protopapadakis
Anastasios Doulamis
Konstantinos Makantasis
Athanasios Voulodimos

Keywords: semi-supervised learning; activity recognition; pattern classification; industrial environments

Abstract:
In this paper, we propose a neural network based scheme for performing semi-supervised job classification, based on video data taken from Nissan factory. The procedure is based on (a) a nonlinear classifier, formed using an island genetic algorithm, (b) a similarity-based classifier, and (c) a decision mechanism that utilizes the classifiers’ outputs in a semi-supervised way, minimizing the expert’s interventions. Such methodology will support the visual supervision of industrial environments by providing essential information to the supervisors and supporting their job.

Pages: 155 to 160

Copyright: Copyright (c) IARIA, 2012

Publication date: October 21, 2012

Published in: conference

ISSN: 2308-3484

ISBN: 978-1-61208-226-4

Location: Venice, Italy

Dates: from October 21, 2012 to October 26, 2012