Multi-sensor fire detection using visual and time-of-flight imaging

This paper proposes a novel multi-sensor fire detection method based on ordinary video images and the amplitude images of a time-of-flight camera. Using this multi-modal in formation, flame regions can be detected very accurately. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are labeled as candidate flame regions. Simultaneously, moving objects in the visual images are also investigated. Objects which possess the experimentally found low-cost flame features are also labeled as candidate flame region. Finally, if one of the visual and amplitude candidate flame regions overlap, fire alarm is given. Experiments show that the proposed detector has an average flame detection rate of 92% with no false positive detections.