This paper proposes two novel time-of-flight based fire detection methods for indoor and outdoor fire detection. The indoor detector is based on the depth and amplitude image of a time-of-flight camera. Using this multi-modal information, flames can be detected very accurately by fast changing depth and amplitude disorder detection. In order to detect the fast changing depth, depth differences between consecutive frames are accumulated over time. Regions which have multiple pixels with a high accumulated depth difference are labeled as candidate flame regions. Simultaneously, the amplitude disorder is also investigated. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are also labeled as candidate flame regions. Finally, if one of the depth and amplitude candidate flame regions overlap, fire alarm is given. The outdoor detector, on the other hand, only differs from the indoor detector in one of its multi-modal inputs. As depth maps are unreliable in outdoor environments, the outdoor detector uses a visual flame detector instead of the fast changing depth detection. Experiments show that the proposed detectors have an average flame detection rate of 94% with no false positive detections.

Being able to model and forecast the fire can help emergency services to work more efficiently and save lives. However, the calculations with current fire modeling techniques, such as CFD and zone models, still take too long and valuable time is often lost. Using the video driven fire spread forecasting methodology proposed in this paper, which is able to give real-time information about the state of the environment, model-based predictions of the future state of a fire can be improved and accelerated. By combining the information about the fire from models and real-time multi-modal LWIR and visual flame and smoke data an estimate of the fire can be produced that is better than could be obtained from using the model or the data alone.

Fire is one of the leading hazards affecting everyday life around the world. The sooner the fire is detected, the better the chances are for survival. Today’s fire alarm systems, such as video-based smoke detectors, however, still pose many problems. In order to accomplish more accurate video-based smoke detection and to reduce false alarms, this paper proposes a multi-sensor smoke detector which takes advantage of the different kinds of information represented by visual and thermal imaging sensors. The detector analyzes the silhouette coverage of moving objects in visual and long-wave infrared registered (~aligned) images. The registration is performed using a contour mapping algorithm which detects the rotation, scale and translation between moving objects in the multi-spectral images. The geometric parameters found at this stage are then further used to coarsely map the silhouette images and coverage between them is calculated. Since smoke is invisible in long-wave infrared its silhouette will, contrarily to ordinary moving objects, only be detected in visual images. As such, the coverage of thermal and visual silhouettes will start to decrease in case of smoke. Due to the dynamic character of the smoke, the visual silhouette will also show a high degree of disorder. By focusing on both silhouette behaviors, the system is able to accurately detect the smoke. Experiments on smoke and non-smoke multi-sensor sequences indicate that the automated smoke detection algorithm is able to coarsely map the multi-sensor images. Furthermore, using the low-cost silhouette analysis, a fast warning, with a low number of false alarms, can be given.

Fire is one of the most powerful forces of nature. Nowadays it is the leading hazard affecting everyday life around the world. The sooner the fire is detected, the better the chances are for survival. Today’s fire alarm systems, such as smoke and heat sensors, however still pose many problems. They are generally limited to indoors; require a close proximity to the fire; and most of them cannot provide additional information about fire circumstances. In order to provide faster, more complete and more reliable information, video fire detection (VFD) is becoming more and more interesting. Current research (Verstockt et al., 2009) shows that video-based fire detection promises fast detection and can be a viable alternative for the more traditional techniques. Especially in large and open spaces, such as shopping malls, parking lots, and airports, video fire detection can make the difference. The reason for this expected success is that the majority of detection systems that are used in these places today suffer with a lot of problems which VFD do not have, e.g., a transportand threshold delay. As soon as smoke or flames occur in one of the camera views, fire can be detected. However, due to the variability of shape, motion, transparency, colors, and patterns of smoke and flames, existing approaches are still vulnerable to false alarms. On the other hand, video-based fire alarm systems mostly only detect the presence of fire. To understand the fire, however, detection is not enough. Effective response to fire requires accurate and timely information of its evolution. As an answer to both problems a multi-sensor fire detector and a multi-view fire analysis framework (Verstockt et al., 2010a) are proposed in this chapter, which can be seen as the first steps towards more valuable and accurate video fire detection. Although different sensors can be used for multi-sensor fire detection, we believe that the added value of IR cameras in the long wave IR range (LWIR) will be the highest. Various facts support this idea. First of all, existing VFD algorithms have inherent limitations, such as the need for sufficient and specific lighting conditions. Thermal IR imaging sensors image emitted light, not reflected light, and do not have this limitation. Also, the further one goes in the IR spectrum the more the visual perceptibility decreases and the thermal perceptibility increases. As such, hot objects like flames will be best visible and less disturbed by other objects in the LWIR spectral range. By combining the thermal and visual

Fire is one of the leading hazards affecting everyday life around the world. The sooner the fire is detected, the better the chances are for survival. Today's fire alarm systems, such as video-based smoke detectors, however, still pose many problems. In order to accom- plish more accurate video-based smoke detection and to reduce false alarms, this paper proposes a multi-sensor smoke detector which takes advantage of the different kinds of information represented by visual and thermal imaging sensors. The detector analyzes the silhouette coverage of moving objects in visual and long-wave infrared registered (∼aligned) images. The registration is performed using a contour mapping algorithm which detects the rotation, scale and translation between moving objects in the multi-spectral images. The geometric parameters found at this stage are then further used to coarsely map the silhouette images and cov- erage between them is calculated. Since smoke is invisible in long-wave infrared its silhouette will, contrarily to ordinary moving objects, only be detected in visual images. As such,

Effective response to fire requires accurate and timely information of its evolution. In order to accomplish this valuable fire analysis step, this work fuses low-cost video fire detection results of multiple cameras using a novel multi-view localization framework. As such, valuable fire characteristics are detected at the early stage of the fire. The framework merges the single-view detection results of the multiple cameras by homographic projection onto multiple horizontal and vertical planes, which slice the scene. The crossings of these slices create a 3D grid of virtual sensor points, called the FireCube. Using this grid and subsequent spatial and temporal 3D clean-up filters, information about the location of the fire, its size and its direction of propagation can be instantly extracted from the video data. The novel aspect in the proposed framework is the 3D grid creation, which is a 3D extension of multiple plane homography. Also the use of spatial and temporal 3D filters, which extend existing 2D filter concepts, provides a more reliable fire analysis. Experimental results indicate that the proposed multi-view fire localization framework is able to accurately detect and localize the fire. Two cameras are already sufficient to achieve a dimension accuracy of 90% and a position accuracy of 98%. By further increasing the number of cameras it is even possible to achieve a dimension accuracy of 96% and a position accuracy of 99%. Furthermore, the experiments show that increasing the number of cameras to monitor the scene has a positive effect on the detection rate. The gain of using four cameras instead of one is 3%.

The possible flow measurement error due to heating or cooling of exhaust gases in the Single-Burning-Item (SBI) test is estimated from numerical experiments. It is illustrated that there is no one-to-one correspondence between the velocity profile shape and the instantaneous Reynolds number, due to the time-dependent temperature and density profile evolution in the exhaust gas pipe. A non-ambiguous relation is found between the velocity profile shape and an 'effective' Reynolds number, based on the turbulent viscosity. Maximum variations of the velocity correction factors, relating the mean velocity to the velocity on the pipe axis, are found to be in the order of 2% for limiting circumstances for the SBI test. The primary effect is caused by instantaneous Reynolds number variations. The effect of heating or cooling of the flow by the hot or cold pipe is noticeable, too. The statements are proved to be valid independent of the computational grid, the turbulence model and the time steps taken to obtain the numerical solutions. Copyright (C) 2006 John Wiley & Sons, Ltd.