Online classification of visual tasks for industrial workflow monitoring

Voulodimos, A; Kosmopoulos, D; Veres, G; Grabner, H; Van Gool, L; Varvarigou, T

dc.contributor.author	Voulodimos, A	en
dc.contributor.author	Kosmopoulos, D	en
dc.contributor.author	Veres, G	en
dc.contributor.author	Grabner, H	en
dc.contributor.author	Van Gool, L	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T01:36:34Z
dc.date.available	2014-03-01T01:36:34Z
dc.date.issued	2011	en
dc.identifier.issn	0893-6080	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21343
dc.subject	Activity recognition	en
dc.subject	ESN	en
dc.subject	Fusion	en
dc.subject	Genetic algorithm	en
dc.subject	HMM	en
dc.subject	Workflow	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Activity patterns	en
dc.subject.other	Activity recognition	en
dc.subject.other	Echo state networks	en
dc.subject.other	ESN	en
dc.subject.other	HMM	en
dc.subject.other	Industrial environments	en
dc.subject.other	Inherent complexity	en
dc.subject.other	Input streams	en
dc.subject.other	Limited visibility	en
dc.subject.other	Multi-cameras	en
dc.subject.other	Novel methods	en
dc.subject.other	On-line classification	en
dc.subject.other	Prior knowledge	en
dc.subject.other	Time series classifications	en
dc.subject.other	Tracking moving targets	en
dc.subject.other	Visual tasks	en
dc.subject.other	Work-flows	en
dc.subject.other	Workflow	en
dc.subject.other	Workflow monitoring	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Time series	en
dc.subject.other	Vision	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	automation	en
dc.subject.other	camera	en
dc.subject.other	classification	en
dc.subject.other	echo state network	en
dc.subject.other	genetic algorithm	en
dc.subject.other	hidden Markov model	en
dc.subject.other	industrial area	en
dc.subject.other	intermethod comparison	en
dc.subject.other	online monitoring	en
dc.subject.other	priority journal	en
dc.subject.other	time series analysis	en
dc.subject.other	visibility	en
dc.subject.other	workflow	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial Intelligence	en
dc.subject.other	Calibration	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Databases, Factual	en
dc.subject.other	Image Processing, Computer-Assisted	en
dc.subject.other	Industry	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Pattern Recognition, Automated	en
dc.subject.other	Video Recording	en
dc.subject.other	Vision, Ocular	en
dc.subject.other	Workflow	en
dc.title	Online classification of visual tasks for industrial workflow monitoring	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.neunet.2011.06.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.neunet.2011.06.001	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	Modelling and classification of time series stemming from visual workflows is a very challenging problem due to the inherent complexity of the activity patterns involved and the difficulty in tracking moving targets. In this paper, we propose a framework for classification of visual tasks in industrial environments. We propose a novel method to automatically segment the input stream and to classify the resulting segments using prior knowledge and hidden Markov models (HMMs), combined through a genetic algorithm. We compare this method to an echo state network (ESN) approach, which is appropriate for general-purpose time-series classification. In addition, we explore the applicability of several fusion schemes for multicamera configuration in order to mitigate the problem of limited visibility and occlusions. The performance of the suggested approaches is evaluated on real-world visual behaviour scenarios. (C) 2011 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Neural Networks	en
dc.identifier.doi	10.1016/j.neunet.2011.06.001	en
dc.identifier.isi	ISI:000295105700009	en
dc.identifier.volume	24	en
dc.identifier.issue	8	en
dc.identifier.spage	852	en
dc.identifier.epage	860	en