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Scene understanding through Deep Learning

  • Recent work in image captioning and scene-segmentation has shown significant results in the context of scene-understanding. However, most of these developments have not been extrapolated to research areas such as robotics. In this work we review the current state-ofthe- art models, datasets and metrics in image captioning and scenesegmentation. We introduce an anomaly detection dataset for the purpose of robotic applications, and we present a deep learning architecture that describes and classifies anomalous situations. We report a METEOR score of 16.2 and a classification accuracy of 97 %.

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Document Type:Report
Author:Luis Octavio Arriaga Camargo
Supervisor:Paul G. Plöger, Matías Valdenegro
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2017/05/29
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science (02-2017)
Keyword:METEOR score; Scene understanding through Deep Learning; image captioning; robotics; scene-segmentation
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2017/05/29
Licence (Multiple languages):License LogoIn Copyright (Urheberrechtsschutz)