Bart Sette

Silhouette-based multi-sensor smoke detection: coverage analysis of moving object silhouettes in thermal and visual registered images

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.

Multi-modal time-of-flight based fire detection

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.

Video driven fire spread forecasting (f) using multi-modal LWIR and visual flame and smoke data

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.

Future directions for video fire detection

To accomplish more valuable and more accurate video fire detection, this paper points out future directions and discusses first steps which are now being taken to improve the vision-based detection of smoke and flames. First, an overview is given of the state-of-the-art detection methods in the visible and infrared spectral range. Then, a novel multi-sensor smoke and flame detector is proposed which combines the multimodal information of low-cost visual and thermal infrared detection results. Experiments on fire and nonfire multi-sensor sequences indicate that the combined detector yields more accurate results, with fewer false alarms, than either detector alone. Next, a framework for multi-view fire analysis is discussed to overcome the lack in a video-based fire analysis tool and to detect valuable fire characteristics at the early stage of the fire. As prior experimental results show, this combined analysis from different viewpoints provides more valuable fire characteristics. Information about 3-D fire location, size and growth rate can be extracted from the video data in practically no time. Finally, directions towards standardized evaluation and video-driven fire forecasting are suggested.