@techreport{ArriagaCamargo2017,
  author    = {Luis Octavio Arriaga Camargo},
  title     = {Scene understanding through Deep Learning},
  isbn      = {978-3-96043-045-2},
  issn      = {1869-5272},
  doi       = {10.18418/978-3-96043-045-2},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-30422},
  institution = {Fachbereich Informatik},
  series    = {Technical Report / University of Applied Sciences Bonn-Rhein-Sieg. Department of Computer Science},
  pages     = {77},
  year      = {2017},
  abstract  = {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 \%.},
  language  = {en}
}