Image Captioning and Classification of Dangerous Situations
- 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.
| Document Type: | Preprint |
|---|---|
| Language: | English |
| Author: | Octavio Arriaga, Paul Plöger, Matias Valdenegro-Toro |
| Pagenumber: | 6 |
| DOI: | https://doi.org/10.48550/arXiv.1711.02578 |
| ArXiv Id: | http://arxiv.org/abs/1711.02578 |
| Publisher: | arXiv |
| Date of first publication: | 2017/11/07 |
| 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: | 2017/11/15 |
