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arXiv · 1711.02578

Image Captioning and Classification of Dangerous Situations

Octavio Arriaga·Paul Plöger·Matias Valdenegro-Toro
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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.

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Octavio Arriaga, Paul Plöger, Matias Valdenegro-Toro. 2017-11-07. Image Captioning and Classification of Dangerous Situations. https://arxiv.org/abs/1711.02578

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