Real-time Fire Research Articles

Fire detectors arrangement in objects with slope roof

Fire detectors present very important elements of all real time fire protection systems. Their assortment, type and arrangement in object present very important task in sense of fire protection system design. This task is often regulated by proper standards. However, some special cases where some changes in standards and design are necessary and obligated, could be found. This paper has written to presents simulation results of heat and smoke detectors reactions in case of object with slope roof.

Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion

In this paper, we propose a method that is able to detect fires by analyzing videos acquired by surveillance cameras. Two main novelties have been introduced. First, complementary information, based on color, shape variation, and motion analysis, is combined by a multiexpert system. The main advantage deriving from this approach lies in the fact that the overall performance of the system significantly increases with a relatively small effort made by the designer. Second, a novel descriptor based on a bag-of-words approach has been proposed for representing motion. The proposed method has been tested on a very large dataset of fire videos acquired both in real environments and from the web. The obtained results confirm a consistent reduction in the number of false positives, without paying in terms of accuracy or renouncing the possibility to run the system on embedded platforms.

New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations

Abstract. Accurate near real time fire emissions estimates are required for air quality forecasts. To date, most approaches are based on satellite-derived estimates of fire radiative power (FRP), which can be converted to fire radiative energy (FRE) which is directly related to fire emissions. Uncertainties in these FRE estimates are often substantial. This is for a large part because the most often used low-Earth orbit satellite-based instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) have a relatively poor sampling of the usually pronounced fire diurnal cycle. In this paper we explore the spatial variation of this fire diurnal cycle and its drivers using data from the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). In addition, we sampled data from the SEVIRI instrument at MODIS detection opportunities to develop two approaches to estimate hourly FRE based on MODIS active fire detections. The first approach ignored the fire diurnal cycle, assuming persistent fire activity between two MODIS observations, while the second approach combined knowledge on the climatology of the fire diurnal cycle with active fire detections to estimate hourly FRE. The full SEVIRI time series, providing full coverage of the fire diurnal cycle, were used to evaluate the results. Our study period comprised of 3 years (2010–2012), and we focused on Africa and the Mediterranean basin to avoid the use of potentially lower quality SEVIRI data obtained at very far off-nadir view angles. We found that the fire diurnal cycle varies substantially over the study region, and depends on both fuel and weather conditions. For example, more "intense" fires characterized by a fire diurnal cycle with high peak fire activity, long duration over the day, and with nighttime fire activity are most common in areas of large fire size (i.e., large burned area per fire event). These areas are most prevalent in relatively arid regions. Ignoring the fire diurnal cycle generally resulted in an overestimation of FRE, while including information on the climatology of the fire diurnal cycle improved FRE estimates. The approach based on knowledge of the climatology of the fire diurnal cycle also improved distribution of FRE over the day, although only when aggregating model results to coarser spatial and/or temporal scale good correlation was found with the full SEVIRI hourly reference data set. We recommend the use of regionally varying fire diurnal cycle information within the Global Fire Assimilation System (GFAS) used in the Copernicus Atmosphere Monitoring Services, which will improve FRE estimates and may allow for further reconciliation of biomass burning emission estimates from different inventories.

Simulation Model of Pedestrian Evacuation in High-Rise Building: Considering Group Behaviors and Real-Time Fire

Many fire evacuation models have been proposed in recent years to better simulate such as an emergency situation. However, fire has different characters in high-rise building, and is affected by all kinds of factors. Evacuation of persons in case of fire is very complex, the environment will affected it, the individual psychological quality and physical quality, real time fire and so on. The numerical model based on cellular automata is presented, considering group behavior and real time fire development in the paper, and simulation experiments show the model is on the brink of reality and demonstrate its applicability and validity.

Computer Vision Based Real-time Fire Detection Method

Computer Vision Based Real-time Fire Detection Method

형상기억합금과 광케이블을 이용한 터널의 화재감지 시스템 개발에 관한 실험적 연구

Recently, design and construction of street tunnels tend to focus on cost reduction and preservation of nature. Accordingly, research is actively being carried out to quickly detect fires when they occur in tunnels, which have partially closed structures. Among such research, fire detection methods using optical fiber sensors have a wide bandwidth and fast transmission speed, while using light as a medium. Therefore, it does not receive electrical interference and there is almost no loss of information during transmission, while also having little noise as well. In relation to this, a fire monitoring system that can accurately detect the location of fires in real time using shape memory alloy and optical cables was developed in this study. In order to verify the developed method, light loss measurement test was conducted according to indoor temperature changes, while also conducting fire simulation tests by installing test beds in common underground zones with different external environments of temperature and distance. Upon carrying out experiments, the fire monitoring system developed in this study was found to be able to detect fires in long distance sections in real time.

Towards a solid solution of real-time fire and flame detection

Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents an empirical study, towards a general and solid approach for fast detection of fire and flame in videos, with the applications in video surveillance and event retrieval. Our system consists of three cascaded steps: (1) candidate regions proposing by a background model, (2) fire region classifying with color-texture features and a dictionary of visual words, and (3) temporal verifying. The experimental evaluation and analysis are done for each step. We believe that it is a useful service to both academic research and real-world application. In addition, we release the software of the proposed system with the source code, as well as a public benchmark and data set, including 64 video clips covered both indoor and outdoor scenes under different conditions. We achieve an 82 % Recall with 93 % Precision on the data set, and greatly improve the performance by state-of-the-arts methods.

An Approach of Failure-Analysis for the Real-Time Fire Reconnaissance Satellite-Monitoring System

In this paper, an approach failure model of Real Time Fire Reconnaissance Satellite-Monitoring System is presented. This approach is also analyzed and proposed based on the Fault Tree Analysis. The methodologies for this design are the Structured Analysis for Real Time SA-RT and the software is designed with the LACATRE formal language. This formal architecture using satellites as the input sensors was adapted from the original model that is a design pattern for Physical variation detection. The original design pattern has the mission of monitoring events such as natural disasters or to look for medical applications, and existing illnesses’ prevention such as diabetes-this is in a patent process. This satellite design will permit Real Time Fire Satellite-Monitoring, which will reduce the damage and danger caused by fire consumption of forests, tropical forests and lands in Mexico. This new proposal makes it possible to have an unused system that impacts on disaster prevention combining national and international technologies and cooperation to the benefit of humankind.

Fire detection system using random forest classification for image sequences of complex background

We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

Fiber Optic Temperature and Gas Concentration Sensor for Mine Goaf Area Inner Detecting

Spontaneous combustion in coal goaf area is one of major disasters in coal mines. Detection technology based on signature Gas and Temperature is the primary means of spontaneous combustion forecasting of coal goaf area. A real-time remote fire detection system is proposed based on tunable diode laser absorption spectroscopy technology and FBG temperature sensing technology, to achieve valid detect of gas concentration and temperature. The System include fiber mathen concentration sensor and fiber temperature sensor based FBG. The system achieved remote on-line monitoring of gas concentration and temperature in mine goaf, meet the fire forecast need for Coal goaf area. There are obvious advantages Compared with the existing beam tube monitoring system in coal mine.

A Real-time Fire Evacuation System with Cloud Computing

An effective evacuation system can help people escape from building fire. Most evacuation systems consist of an indoor positioning system, a back-end database, and a display device with calculation and display software. However, very few of them can smartly determine which evacuation route is the best decision. If all the locations of the evacuating people can be simultaneously determined, the best evacuation routes can be decided to avoid congestion, and survival rate can increase. The previous radio frequency identification (RFID) based evacuation system focused on detecting the RFID tags using a mobile phone in order to determine the location of the mobile phone user so that an evacuation route can be displayed. However, the system is available for one person regardless of the number of evacuating people or exits. This study is based on the previous RFID based evacuation system investigating the best evacuation routes. The system introduces cloud computing that calculates for positioning the evacuating people and determining the optimum evacuation routes for each of them. The system will be implemented at Tamkang University on Lanyang campus.

Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power

Abstract. The Global Fire Assimilation System (GFASv1.0) calculates biomass burning emissions by assimilating Fire Radiative Power (FRP) observations from the MODIS instruments onboard the Terra and Aqua satellites. It corrects for gaps in the observations, which are mostly due to cloud cover, and filters spurious FRP observations of volcanoes, gas flares and other industrial activity. The combustion rate is subsequently calculated with land cover-specific conversion factors. Emission factors for 40 gas-phase and aerosol trace species have been compiled from a literature survey. The corresponding daily emissions have been calculated on a global 0.5° × 0.5° grid from 2003 to the present. General consistency with the Global Fire Emission Database version 3.1 (GFED3.1) within its accuracy is achieved while maintaining the advantages of an FRP-based approach: GFASv1.0 makes use of the quantitative information on the combustion rate that is contained in the FRP observations, and it detects fires in real time at high spatial and temporal resolution. GFASv1.0 indicates omission errors in GFED3.1 due to undetected small fires. It also exhibits slightly longer fire seasons in South America and North Africa and a slightly shorter fire season in Southeast Asia. GFASv1.0 has already been used for atmospheric reactive gas simulations in an independent study, which found good agreement with atmospheric observations. We have performed simulations of the atmospheric aerosol distribution with and without the assimilation of MODIS aerosol optical depth (AOD). They indicate that the emissions of particulate matter need to be boosted by a factor of 2–4 to reproduce the global distribution of organic matter and black carbon. This discrepancy is also evident in the comparison of previously published top-down and bottom-up estimates. For the time being, a global enhancement of the particulate matter emissions by 3.4 is recommended. Validation with independent AOD and PM10 observations recorded during the Russian fires in summer 2010 show that the global Monitoring Atmospheric Composition and Change (MACC) aerosol model with GFASv1.0 aerosol emissions captures the smoke plume evolution well when organic matter and black carbon are enhanced by the recommended factor. In conjunction with the assimilation of MODIS AOD, the use of GFASv1.0 with enhanced emission factors quantitatively improves the forecast of the aerosol load near the surface sufficiently to allow air quality warnings with a lead time of up to four days.

Marine and biomass burning aerosols in the southern Indian Ocean: Retrieval of aerosol optical properties from shipborne lidar and Sun photometer measurements

We document aerosol extinction properties in the southern Indian Ocean. A unique data set of shipborne measurements has been collected with a dual Rayleigh-Mie lidar aboard the research vessel Marion Dufresne during two campaigns: one around Madagascar during the Southern Hemisphere late summer and one close to the Kerguelen Islands during the biomass burning (BB) season. During this latter, a layer containing a mix of BB and marine aerosols extending up to ∼3 km above mean sea level (amsl) has been observed from [31°S, 69°E] to [24°S, 59°E]. Both vertical structure and aerosol optical properties have been retrieved from the inversion of the lidar signals. Sun photometer-derived aerosol optical thickness (AOT) at 355 nm is used to constrain the lidar inversion. We obtain a mean integrated value of backscatter-to-extinction ratio (BER) (extinction-to-backscatter ratio, or so-called lidar ratio, LR) of 0.039 ± 0.009 sr−1 (26 ± 6 sr) and 0.021 ± 0.006 sr−1 (48 ± 12 sr) for the marine aerosols layer, and for the mixing between BB and marine aerosols with an uncertainty of 0.009 sr−1 (6 sr) and 0.004 sr−1 (9 sr), respectively. Lidar calibration is used to inverse data without any simultaneous Sun photometer measurements (as nighttime data), and the temporal evolution of the optical properties and vertical extension of the BB aerosol plume is documented. The presence of BB aerosols is in agreement with Lagrangian model GIRAFE v3 (reGIonal ReAl time Fire plumEs) simulations, which show the South American and Southern African BB origin of the encountered aerosol layer.

The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010)

Between 2006 and 2010, National Aeronautics and Space Administration (NASA) and the US Forest Service flew 14 unmanned airborne system (UAS) sensor missions, over 57 fires in the western US. The missions demonstrated the capabilities of a UAS platform (NASA Ikhana UAS), a multispectral sensor (autonomous modular sensor (AMS)), onboard processing and data visualization (Wildfire Collaborative Decision Environment (W-CDE)), to provide fire intelligence to management teams. Autonomous, on-board processing of the AMS sensor data allowed real-time fire product delivery to incident management teams on the wildfire events. The fire products included geo-rectified, colour-composite quick-look imagery, fire detection shape files, post-fire real-time normalized burn ratio imagery and burn area emergency response (BAER) imagery. The W-CDE was developed to allow the ingestion and visualization of AMS data and other pertinent fire-related information layers. This article highlights the technologies developed and employed, the UAS wildfire imaging missions performed and the outcomes and findings of the multi-year efforts.

An integrated fire detection and suppression system based on widely available video surveillance

An integrated system based on video surveillance is presented for automatic fire detection and suppression. The system is composed of two modules, including fire detection and fire suppression. The fire detection module makes full use of traditional CCD cameras for fire recognition. Some spatio-temporal features, such as color and motion, are extracted to detect fire in real time by utilizing sequential image processing techniques. Once a fire is detected, the system will control the fire suppression module to extinguish the fire automatically. It mainly consists of control device, mobile device, and water gun. Experiments performed in a large space hall show that the integrated system can detect a fire about a few seconds after ignition and automatically suppress the fire quickly.

Real-time notification and improved situational awareness in fire emergencies using geospatial-based publish/subscribe

Emergency agencies seek to maintain situational awareness and effective decision making through continuous monitoring of, and real-time alerting about, sources of information regarding current incidents and developing fire hazards. The nature of this goal requires integrating different, potentially numerous, sources of dynamic geospatial information on the one side, and a large number of clients having heterogeneous and specific interests in data on the other side. In such scenarios, the traditional request/reply communication style may function inefficiently, as it is based on point-to-point, synchronous, and pulling mode interaction between consumer clients and information providers/services. In this work, we propose Geospatial-based Publish/Subscribe, an interaction framework that serves as a middleware for real-time transacting of spatially related information of interest, termed geospatial events, in distributed systems. Expressive data models, including geospatial event and geospatial subscription, as well as an efficient matching approach for fast dissemination of geospatial events to interested clients, are introduced. The proposed interaction framework is realized through the development of a Real-Time Fire Emergency Response System (RFERS) prototype. The prototype is designed for transacting several topics of geospatial events that are crucial within the context of fire emergencies, including GPS locations of emergency assets, meteorological observations of wireless sensors, fire incidents reports, and temporal sequences of remote sensing images of active wildfires. The performance of the system prototype has been evaluated in order to demonstrate its efficiency.

REAL-TIME FIRE AND SMOKE DETECTION USING CAMERA SEQUENCE IMAGE IN TUNNEL ENVIRONMENT

In this paper, we proposed an image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurring in the tunnel, it is necessary to have a system to sense and minimize the incident as fast as possible. However it is impossible for human observation of Closed-Circuit Television (CCTV) in tunnel for 24 h. So if the fire and smoke detection system through image processing can warn a fire, it will be very convenient, and it can be possible to minimize damage even when no one is in front of the monitor. The fire and smoke detection is different from forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so an indigenous algorithm has to be developed. The two algorithms proposed in this paper are able to detect the exact position at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.

Leptin Regulation of the Mesoaccumbens Dopamine Pathway

Leptin is an adipose-derived hormone that acts on hypothalamic leptin receptors to regulate energy balance. Leptin receptors are also expressed in extrahypothalamic sites including the ventral tegmental area (VTA), critical to brain reward circuitry. We report that leptin targets DA and GABA neurons of the VTA, inducing phosphorylation of signal-transducer-and-activator-of-transcription-3 (STAT3). Retrograde tracing combined with pSTAT3 immunohistochemistry show leptin-responsive VTA neurons projecting to nucleus accumbens (NAc). Assessing leptin function in the VTA, we showed that ob/ob mice had diminished locomotor response to amphetamine and lacked locomotor sensitization to repeated amphetamine injections, both defects reversed by leptin infusion. Electrically stimulated DA release from NAc shell terminals was markedly reduced in ob/ob slice preparations, and NAc DA levels and TH expression were lower. These data define a role for leptin in mesoaccumbens DA signaling and indicate that the mesoaccumbens DA pathway, critical to integrating motivated behavior, responds to this adipose-derived signal.

Computer vision based method for real-time fire and flame detection

This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues.

Forest fire scar detection in the boreal forest with multitemporal spot-vegetation data

Disturbance events, such as fire, have a major impact on boreal forest dynamics, succession, and the global carbon cycle. Methods using satellite imagery are well established for detecting forest fires in real time and mapping the burned area (fire scars) within one year of the fire. This paper focuses on the detection of older fire disturbance-regeneration patterns in the boreal forests of Canada. Previous work found that shortwave-infrared image segmentation proved particularly good at creating uniform regions that were easy to associate with fire scars. Our findings suggest it is possible to detect fire scars up to ten years old using SPOT-VEGETATION data from a single year and that the use of a vegetation index based on near- and shortwave-infrared reflectance is critical to this success. We demonstrate how the use of short-term multitemporal imagery can enhance segmentation results and present a threshold-based procedure for a posteriori identification of fire scar segments. The resulting fire scar probability map showed a good correspondence with records of fire scars mapped by the Canadian Forest Service for 1980-1992 and "hot spots" from the FireM3 Information System for 1994-1998.