Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video

TL;DR

This paper introduces a real-time flame detection method in videos using a specialized deep convolutional GAN that leverages spatio-temporal information, improving robustness and reducing false positives in surveillance applications.

Contribution

It presents a novel two-stage training framework for DCGANs that incorporates spatio-temporal flame evolution, enhancing flame detection accuracy in videos.

Findings

Effective real-time flame detection with low false positives

Robust representation of flame sequences using spatio-temporal data

Superior performance compared to existing methods

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.