Autonomous flame detection in video based on saliency analysis and optical flow

The paper proposes a flame detection method based on saliency analysis, optical flow estimation and temporal wavelet transform. Two separate saliency maps are first obtained based on the grayscale values and optical flow magnitudes of each frame using a saliency detector. Subsequently, the two maps are combined to extract candidate flame regions. To further discard falsely detected pixels, a colour model of flames and temporal wavelet transform are employed. The proposed algorithms can be applied in the autonomous and semi-autonomous systems for environmental surveillance and can reduce the load of human operators. Experiments illustrate the introduced method achieves around 91% true positive rate and 97% true negative rate.