arXiv:1711.02578cs.CV
Image Captioning and Classification of Dangerous Situations
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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.
There are a couple papers from 2017 where they talk about getting some effective results, but there is a lack of datasets around this topic and that the area is less studied. Can you point to the efforts that have been made here?…
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