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.
@article{arxiv.1711.02578,
title = {Image Captioning and Classification of Dangerous Situations},
author = {Octavio Arriaga and Paul Plöger and Matias Valdenegro-Toro},
journal= {arXiv preprint arXiv:1711.02578},
year = {2017}
}