Using real data from Flickr, our results showed that FFireDt was able to achieve a precision for fire detection that was comparable to that of human annotators. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFireDt), which combines feature extractor and evaluation functions to support instance-based learning (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences – the Flickr-Fire dataset and (iii) the evaluation of 36 efficient image descriptors for fire detection.
To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Despite the many works on image analysis, there are no fire detection studies on social media. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. (More)Ĭrowdsourcing and social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media and crowdsourcing.
Subjects/Areas/Topics: Applications of Expert SystemsĪrtificial Intelligence and Decision Support Systemsĭatabases and Information Systems IntegrationĪbstract: Crowdsourcing and social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. Keyword(s): Fire Detection, Feature Extraction, Evaluation Functions, Image Descriptors, Social Media.
Traina and Caetano Traina Jr.Īffiliation: University of São Paulo, Brazil
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