Conference paper Open Access

People and vehicles in danger - A fire and flood detection system in social media

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

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
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Publication date:
June 10, 2018
DOI:
10.5281/zenodo.1243993

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10.5281/zenodo.1243993

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Keyword(s):
computer vision deep learning image segmentation object detection
Grants:
European Commission:
  • EOPEN - EOPEN: opEn interOperable Platform for unified access and analysis of Earth observatioN data (776019)
  • beAWARE - Enhancing decision support and management services in extreme weather climate events (700475)
Meeting:
IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), Zagori, Aristi Village Greece, 10-12 June 2018
License (for files):
Creative Commons Attribution 4.0 International

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