Multi-attribute Based Fire Detection in Diverse Surveillance Videos

Fire detection, as an immediate response of fire accident to avoid great disaster, has attracted many researchers’ focuses. However, the existing methods cannot effectively exploit the comprehensive attribute of fire to give satisfying accuracy. In this paper, we design a multi-attribute based fire detection system which combines the fire’s color, geometric, and motion attributes to accurately detect the fire in complicated surveillance videos. For geometric attribute, we propose a descriptor of shape variation by combining contour moment and line detection. Furthermore, to utilize fire’s instantaneous motion character, we design a dense optical flow based descriptor as fire’s motion attribute. Finally, we build a fire detection video dataset as the benchmark, which contains 305 fire and non-fire videos, with 135 very challenging negative samples for fire detection. Experimental results on this benchmark demonstrate that the proposed approach greatly outperforms the state-of-the-art method with 92.30% accuracy and only 8.33% false positives.