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- W2920320876 abstract "Real-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 substantial amount of labeled data. In order 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." @default.
- W2920320876 created "2019-03-11" @default.
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- W2920320876 date "2019-02-05" @default.
- W2920320876 modified "2023-10-17" @default.
- W2920320876 title "Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video." @default.
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- W2920320876 hasPublicationYear "2019" @default.
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