Computer vision-based early fire detection using enhanced chromatic segmentation and optical flow analysis technique

Abstract

Recent advances in video processing technologies have led to a wave of research on computer vision-based fire detection systems. This paper presents a multi-level framework for fire detection that analyses patterns in chromatic information, shape transmutation, and optical flow estimation of fire. First, the decision function of fire pixels based on chromatic information uses majority voting among state-of-the-art fire color detection rules to extract the regions of interest. The extracted pixels are then verified for authenticity by examining the dynamics of shape. Finally, a measure of turbulence is assessed by an enhanced optical flow analysis algorithm to confirm the presence of fire. To evaluate the performance of the proposed model, we utilize videos from the Mivia and Zenodo datasets, which have a diverse set of scenarios including indoor, outdoor, and forest fires, along with videos containing no fire. The proposed model exhibits an average accuracy of 97.2% for our tested dataset. In addition, the experimental results demonstrate that the proposed model significantly reduces the rate of false alarms compared to the other existing models