Stereo-Vision-Based Fire Detection and Suppression Robot for Buildings

Stereo-Vision-Based Fire Detection and Suppression Robot for Buildings

Chao-Ching Ho (National Yunlin University of Science and Technology, Taiwan)
Copyright: © 2012 |Pages: 15
DOI: 10.4018/978-1-61350-326-3.ch022

Abstract

A stereo-vision-based fire detection and suppression robot with an intelligent processing algorithm for use in large spaces is proposed in this chapter. The successive processing steps of our real-time algorithm use the motion segmentation algorithm to register the possible position of a fire flame in a video; the real-time algorithm then analyzes the spectral, spatial, and motion orientation characteristics of the fire flame regions from the image sequences of the video. The characterization of a fire flame was carried out by using a heuristic method to determine the potential fire flame candidate region. The fire-fighting robot uses stereo vision generated by means of two calibrated cameras to acquire images of the fire flame and applies the continuously adaptive mean shift (CAMSHIFT) vision-tracking algorithm to provide feedback on the real-time position of the fire flame with a high frame rate. Experimental results showed that the stereo-vision-based mobile robot was able to successfully complete a fire-extinguishing task.
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Background

In recent times, research on the detection of fire flames using surveillance cameras with machine vision has gained momentum. The image processing approach involves the extraction of the fire flame pixels from a background by using frame difference technologies. Healey et al. (1993) presented a fire detection algorithm using a color video input with a pre-partitioning scheme under some restricted conditions, without rejecting the similar fire-like alias. Phillips et al. (2002) and Celik et al. (2007) conducted studies on computer vision by using spectral analysis and the flickering property of fire flame pixels to recognize the existence of fires at a scene. Hue and saturation are adopted as feature vectors to extract the fire pixels from the visual images (Chen, 2003). Fire flame features based on the HSI(hue, saturation, intensity) color model are extracted, and regions with fire-like colors are roughly separated from the image by the color separation method (Horng, 2005). Then, the image difference method based on chromatics is used to remove spurious fire-like regions such as objects with similar fire colors or areas reflected from fire flames. A fuzzy-based dominant flame color lookup table is created, and fire regions are automatically selected (Wang, 2006). However, either the solution does not consider the temporal variation of flames or the approach is too complicated to process in real time.

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