Deep convolutional generative adversarial networks for flame detection in video

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buir.contributor.authorAslan, Süleyman
buir.contributor.authorGüdükbay, Uğur
buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage815en_US
dc.citation.spage807en_US
dc.citation.volumeNumber12496 LNAIen_US
dc.contributor.authorAslan, Süleymanen_US
dc.contributor.authorGüdükbay, Uğuren_US
dc.contributor.authorTöreyin, B. U.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialDa Nang, Vietnamen_US
dc.date.accessioned2021-02-05T13:21:09Z
dc.date.available2021-02-05T13:21:09Z
dc.date.issued2020
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 30 November - 3 December 2020en_US
dc.descriptionConference name: 12th International Conference on Computational Collective Intelligence, ICCCI 2020en_US
dc.description.abstractReal-time flame detection is crucial in video-based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require a substantial amount of labeled data. To have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatio-temporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false-positive rates in real-time.en_US
dc.description.sponsorshipA. Enis Çetin’s research is partially funded by NSF with grant number 1739396 and NVIDIA Corporation. B. Uğur Töreyin’s research is partially funded by TÜBİTAK 114E426, İTÜ BAP MGA-2017-40964 and MOA-2019-42321.en_US
dc.identifier.doi10.1007/978-3-030-63007-2_63en_US
dc.identifier.isbn9783030630065
dc.identifier.urihttp://hdl.handle.net/11693/55013
dc.language.isoEnglishen_US
dc.publisherSpringer, Chamen_US
dc.relation.ispartofseriesLecture Notes in Computer Science;LNCS 12496
dc.relation.isversionofhttps://doi.org/10.1007/978-3-030-63007-2_63en_US
dc.source.titleComputational Collective Intelligence: 12th International Conference, ICCCI 2020en_US
dc.subjectFire detectionen_US
dc.subjectFlame detectionen_US
dc.subjectDeep convolutional generative adversarial neural networken_US
dc.titleDeep convolutional generative adversarial networks for flame detection in videoen_US
dc.typeConference Paperen_US

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