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A Computer Vision-Based Method for Fire Detection in Color Videos

Xiaojun Qi, Jessica Ebert

Abstract


This paper presents a computer vision-based approach for automatically detecting the presence of fire in video sequences. The algorithm not only uses the color and movement attributes of fire, but also analyzes the temporal variation of fire intensity, the spatial color variation of fire, and the tendency of fire to be grouped around a central point. A cumulative time derivative matrix is used to detect areas with a high frequency luminance flicker. The fire color of each frame is aggregated in a cumulative fire color matrix using a new color model which considers both pigmentation values of the RGB color and the saturation and the intensity properties in the HSV color space. A region merging algorithm is then applied to merge the nearby fire colored moving regions to eliminate the false positives. The spatial and temporal color variations are finally applied to detect fires. Our extensive experimental results demonstrate that the proposed system is effective in detecting all types of uncontrolled fire in various situations, lighting conditions, and environment. It also performs better than the peer system with higher true positives and true negatives and lower false positives and false negatives.

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