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People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

Authors: Panagiotis Giannakeris; Konstantinos Avgerinakis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris;

People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

Abstract

This paper presents a novel warning system framework for detecting people and vehicles in danger. The system was tested in several images compiled from Flickr and other social media sources and is highly suggested to get integrated in future warning surveillance and safety systems for preventing or solving crisis events. The proposed framework recruits State-ofthe-Art deep learning technologies so as to solve a series of image processing and machine learning challenges and provides a near real-time localization solution for detecting and scoring severity safety levels of people and vehicles in flood and fire images.

This work was supported by beAWARE and EOPEN project partially funded by the European Commission under grant agreement No 700475 and 776019

Keywords

deep learning, object detection, image segmentation, computer vision

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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15
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5
30
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