@unpublished{ArriagaPloegerValdenegroToro2017,
  author  = {Octavio Arriaga and Paul Pl{\"o}ger and Matias Valdenegro-Toro},
  title   = {Image Captioning and Classification of Dangerous Situations},
  institution = {Fachbereich Informatik},
  pages     = {6},
  year    = {2017},
  abstract  = {Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 \% and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted.},
  language  = {en}
}