  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.
