Fire Video Research Articles

Fire Detection using Color and Motion Models

This paper presents a fire detection algorithm using color and motion models from video sequences. The proposed method detects change in color and motion of overall regions for detecting fire, and thus, it can be implemented in both fixed and pan/tilt/zoom (PTZ) cameras. The proposed algorithm consists of three parts. The first part exploits color models of flames and smoke. The candidate regions in the video frames are extracted with the hue-saturation-value (HSV) color model. The second part models the motion information of flames and smoke. Optical flow in the fire candidate region is estimated, and the spatial–temporal distribution of optical flow vectors is analyzed. The final part accumulates the probability of fire in successive video frames, which reduces false-positive errors when fire-like color objects appear. Experimental results from 100 fire videos are shown, where various types of smoke and flames appear in indoor and outdoor environments. According to the experiments and the comparison, the proposed fire detection algorithm works well in various situations, and outperforms the conventional algorithms.

Research on Flame Image Detection

Fire flame detection can reduce the damage of people’s life and property in maximum. We analysis the significant characteristics such as color feature, sharp corners, flicker frequency, change of area, flame core features such as the relative area in the suspected areas as fire classification. Through the characteristics of the fire video image analysis, first, select color features to pre-judge, then couple with area change rate and sharp corners to determine the false fire target, last, adapt the algorithm to tall space, outdoor environment and some fire detection under complicated environment. Comparing with traditional detection, the algorithm can find the fire accurately and timely, reduce the amount of calculation, improves fire recognition accuracy, and reduce false positives omission phenomenon.

Video fire detection based on Gaussian Mixture Model and multi-color features

This paper proposes a new approach to detect fire from a video stream. It takes full advantage of the motion feature and color information of fire. Firstly, motion detection using Gaussian Mixture Model-based background subtraction is applied to extract moving objects from a video stream. Then, multi-color-based detection combining the RGB, HSI and YUV color space is employed to obtain possible fire regions. Finally, the results of the above two steps are combined to identify the accurate fire areas. The experimental results obtained by applying this method on different fire videos show that the proposed method can achieve better effectiveness, adaptability and robustness.

Detecting fire in video stream using statistical analysis

The real time fire detection in video stream is one of the most interesting problems in computer vision. In fact, in most cases it would be nice to have fire detection algorithm implemented in usual industrial cameras and/or to have possibility to replace standard industrial cameras with one implementing the fire detection algorithm. In this paper, we present new algorithm for detecting fire in video. The algorithm is based on tracking suspicious regions in time with statistical analysis of their trajectory. False alarms are minimized by combining multiple detection criteria: pixel brightness, trajectories of suspicious regions for evaluating characteristic fire flickering and persistence of alarm state in sequence of frames. The resulting implementation is fast and therefore can run on wide range of affordable hardware.

Real‐time multi‐feature based fire flame detection in video

In this study, the authors present a new approach to detect fire flame by processing and analysing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi-feature, i.e. chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In their approach, the authors adopt a novel algorithm to extract the moving region and analyse the frequency of flickers. Experimental results show that the proposed method can run in real-time and performs favourably against the state-of-the-art methods with higher accuracy in fire videos, lower false alarm rates in non-fire videos and faster response time.

N-Heptane Pool Fire Suppression with Twin-Fluid Water Mist

As one of the most promising halon alternatives, the merits of water mist have been demonstrated by prior research and tests. Much research done before has been focusing on the improvement of some aspects of water mist systems such as the shape of nozzle, the size of mist particles, and the additive used etc. In order to study the effectiveness of twin-fluid water mist technology and the role of nitrogen in fire suppression, a series of n-heptane pool fire suppression tests were carried out in this paper. The parameters such as fuel mass loss, flame temperature above the centerline of the pool, and axial heat fluxes were measured for assistant analysis of fire suppression during the tests as well as the fire video recorded. All suppression times were analyzed statistically based on the video and they all were under 30 seconds. Results show that the higher Nitrogen pressure is, the less the suppression time is, which means better suppression effect.

Fast Video Fire Detection Using Luminous Smoke and Textured Flame Features

In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.

영상화재감지기 시험과 설치기준에 관한 연구

본 논문은 영상화재감지기(Video Fire Detector)의 시험 및 설치기준에 관한 연구로서 FM Approvals, UL, ISO7240 및 NFPA 72 기준들을 서로 비교분석하여 국가화재안전기준 203 (NFSC 203)에 반영되어야 할 기준들을 제안하였다. 영상화재감지기의 시험과 관계된 FM Approvals 기준에는 연기형, 불꽃형으로 분류되고 비화재보시험이 있었으나 UL 기준에는 연기형만 제시되어 있었다. 외국 기준인 ISO 7240에서는 화재현상 검출유형 등 6가지를, NFPA 72에서는 화재현상 검출유형 등 3가지를 조사하였다. CCTV 설치 표준공법에서는 영상화재감지기의 설치기준에 필요한 항목이 15가지가 있었으며 설치방법에 따라 각 분야별 기준이 제시되어 있었다. 영상화재감지기 적용을 위해서는 감지기 시험항목 중 용어의 정의, 감지기의 구분, 구조 및 기능에 관련항목을 삽입하고 감도조정, 비화재보방지, 주위온도시험, 유효감지거리 및 감도와 시야각, 노화시험, 살수시험, 내화시험의 7개 항목의 적용이 필요하다. 현장 설치를 위해서는 국가화재안전기준 203 (NFSC 203)에 작동환경 및 조도기준을 설정하고 음향장치, 표시등의 설치거리 등의 기준을 삽입하여야 한다. This research performed tests of Video Fire Detector and criteria of installation to make suggestions regarding the criteria that must be reflected in NFSC 203 by comparing the standards of FM Approvals, UL, ISO7240 and NFPA 72. FM Standard related to Video Fire Detector test has been classified as Smoke, Flame type, but the UL Standard has classified only as a Smoke type. This research examined 6 cases of fire phenomenon detection case in ISO 7240 and 3 cases in NFPA 72, respectively. There are 15 items required for the installation standard of a Video Fire Detector and each field standard is presented as a per installation method. To apply a Video Fire Detector, the pertinent items (the definition of term, detector's classification, structure and function among its test item) must be inserted. In addition, 7 items of the fire test, i.e., the sensitivity adjustment, prevent false alarm, ambient temperature test, the effective sensitivity and detection distance and viewing angle, aging test, flood test, must be applied to the actual test. For installation in the field, the operation environment and levels of illumination, and NFSC 203 must be set, and standards relevant to the sound system, indicators' installation distance, etc. need to be inserted.

Developing Skills in Digital Contexts

The use of commercial video games in combination with other media can provide opportunities to learn skills related to critical thinking, digital literacy, and media production. These media tools offer opportunities for users to participate in the new emerging forms of participatory culture. By means of an ethnographic study carried out in a Spanish primary school, this article presents an analysis of the skills that can be developed through the use of the Harry Potter and the Goblet of Fire video game and movie and how education professionals can use them at school in order to change the way students learn. The results show how students can develop their critical capacity by comparing these two media, and that this enables them to develop processes geared to digital literacy at the same as it helps them become producers through the creation and publication of blogs.

Light Condition Estimation Based on Video Fire Detection in Spacious Buildings

In industrial applications in spacious buildings, video-based fire detection system needs to endure the excessive incoming light, which makes the video overexposed. So an adaptive flame segmentation and recognition algorithm is proposed to promote the adaptability and detection rate of the video-based fire detection system for a spacious building. First, moving foreground in a video is found and luminance of the moving region is calculated to estimate the light condition. For different light conditions, different flame-color segmentation models are selected adaptively. After a series of post-processes of segmentation, the suspect flame regions are extracted for feature analysis. Then, a trained support vector machine is implemented to distinguish flame and non-flame regions. The performance of the proposed algorithm is verified on a set of videos containing flames and interference. The adaptive flame segmentation model promotes the flame segmentation resulting in different light conditions. The results are compared with those of three other methods used in the literature, revealing the proposed method to have both a better segmentation result and better precision. In flame classification, the performance of five other methods has been compared with that of the proposed SVM method, and the result shows that the SVM classifier has the best stability and the accuracy is higher than most of the other tests. The proposed method achieves average detection rate of 95.0 %. The result shows that both the accuracy and robustness of segmentation have been improved and it is appropriate for industrial fire detection in spacious buildings.

Flame Spread Monitoring and Estimation of the Heat Release Rate from a Cable Tray Fire Using Video Fire Analysis (VFA)

The paper presents an application of video fire analysis (VFA) to a cable tray fire scenario, a scenario of interest to nuclear power plants where cables constitute a substantial part of the combustible load. Five horizontal trays, each 2.4 m long and 0.45 m wide, are positioned with a 0.3 m spacing (in the vertical direction) and set-up against an insulated wall. Each tray contains 49 power PVC cables of 13 mm outer diameter. Ignition is performed with an 80 kW propane burner centrally positioned at 0.2 m below the first (i.e. lowest) tray. Thanks to a flame detection algorithm, the VFA technique allows a reconstruction of the heat release rate (HRR) profile based on the estimated temporal evolution of flame and extinction fronts at the level of each tray. The obtained profile is generally in good agreement with the HRR measurements (from oxygen depletion calorimetry). However, the time to reach the peak HRR is overestimated by approximately 100 s and the peak HRR of 3 MW is underestimated by 17%. These results are nevertheless encouraging because they provide confidence in the flame spread measurements (which are estimated for the case at hand between 2 mm/s and 5 mm/s). Therefore, a systematic application of VFA to real-scale experiments is believed to generate a valuable set of data (e.g. flame spread rates) for the development of simplified modelling.

A dual-band flame detector based on video

High quality medium band-pass infrared filters are often adopted in optical systems to improve signal-to-noise ratio. In order to extract the flame feature more easy, a band-pass film is designed and fabricated with using germanium (Ge) and silicon oxide (SiO) as the higher and the lower refractive index materials, respectively. The selection of materials and the design principles of the mid-infrared band-pass filter were introduced in detail, the film structure is also given. Then a dual-band flame detector was developed based on video. The TI DaVinci technology was used to process fire video and then regional connectivity sign algorithm was employed to detect the edge contour for obtaining the parameters of the flame circle. Realization of four modules for the proposed flame detector is introduced. All the hardware was designed, and the circuit board of the flame detector was divided into two part boards. Furthermore, it realized the software for the flame detector. Finally, an experiment was done to test the detector. The results show that the developed detector can find the fire quickly.

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.

Global analysis of multi-compartment full-scale fire tests (‘Rabot2012’)

A global analysis is presented of a series of four multi-compartment full-scale fire tests carried out in an apartment located in a high-rise building (called the ‘Rabot’ tower in the city of Ghent, Belgium) in 2012. The data set, available at http://multimedialab.elis.ugent.be/rabot2012/, is called ‘Rabot2012’. These tests are believed to provide a valuable set of experimental data for fire modelling (CFD and two-zone) and monitoring purposes, for a number of reasons. First of all, the fuel package of intermediate complexity (i.e. two to three furniture items) and the ignition location were designed to focus on the initial flame spread and then the possible occurrence of secondary (and in one test tertiary) ignition. Also, very good repeatability is illustrated during the first minutes of the tests. Regarding the sensing aspect, in addition to the conventional tools such as thermocouples and velocity probes, a Video Fire Analysis (VFA) has been performed by applying smoke and flame detection algorithms to the footages obtained from a multi-view video network. Moreover, laboratory tests have been performed, putting the fuel packages as applied in the full-scale tests under a hood in free-burning conditions. The repeatability during the first minutes is confirmed and subsequent differences in fire development (free burning versus compartment set-up) are explained. This presentation of the ‘Rabot2012’ fire tests comprises: (1) a detailed description of the experimental set-up (i.e. geometry, fuel package and instrumentation), (2) an overview of the fire scenarios obtained in each of the four tests, and (3) a detailed account of the data processing carried out. Finally, elements of exploitation of the collected set of data are discussed.

Early smoke detection of forest fire video using CS Adaboost algorithm

Smoke is an important indicator of forest fire video. However, many distractors such as heavy fog and smoke-like moving objects greatly degrade the recognition accuracy. This paper presents a novel smoke detection method of early forest fire video using CS Adaboost algorithm. First, motion regions are extracted from two adjacent frames by a proper background model which can avoid false positives of some static distractors, such as blue sky and gray leaves. Then, a CS Adaboost algorithm is used to recognize smoke regions using centroid movement by means of smoke flutter, image energy on the basis of the Wavelet Transform coefficients and color information between a reference smoke color and the input frame. Finally, the experimental results show that the proposed method can not only detect smoke image of early low thickness, but also more effectively distinguish dense fog from smoke.

GPU를 이용한 다양한 해상도의 비디오기반 실시간 화재감지 방법 구현 및 성능평가

본 논문에서는 기존에 많이 사용되는 복잡한 4단계 화재 감지 알고리즘의 성능을 향상시키기 위해 그래픽스 처리 장치 (GPU)를 이용한 효율적인 병렬 구현 방법을 제안하였고 성능을 분석하였다. 또한 현재 많이 사용되고 있는 7가지 서로 다른 해상도 (QVGA, VGA, SVGA, XGA, SXGA+, UXGA, QXGA)의 비디오를 입력으로 하여 성능을 분석하였다. 더불어 각 해상도별 GPU 기반 실행시간과 고성능 CPU에서의 실행시간을 비교 분석하였다. 각 해상도의 5가지 화재 및 비 화재 비디오를 이용하여 모의 실험한 결과, GPU는 CPU보다 실행시간에서 우수한 성능을 보이는 동시에 FULL HD급의 높은 해상도인 UXGA 영상에서도 프레임 당 25.11ms의 실행시간이 소요되어 초당 30 프레임의 실시간 처리가 가능함을 보였다. In this paper, we propose an efficient parallel implementation method of a widely used complex four-stage fire detection algorithm using a graphics processing unit (GPU) to improve the performance of the algorithm and analyze the performance of the parallel implementation method. In addition, we use seven different resolution videos (QVGA, VGA, SVGA, XGA, SXGA+, UXGA, QXGA) as inputs of the four-stage fire detection algorithm. Moreover, we compare the performance of the GPU-based approach with that of the CPU implementation for each different resolution video. Experimental results using five different fire videos with seven different resolutions indicate that the execution time of the proposed GPU implementation outperforms that of the CPU implementation in terms of execution time and takes a 25.11ms per frame for the UXGA resolution video, satisfying real-time processing (30 frames per second, 30fps) of the fire detection algorithm.

Forest Fire Prevention Video Data Transmission System Design Based on Wireless Mesh Network

For forest fire prevention video monitoring data collection requirements,making video data collecting transmission system design based on wireless mesh network;including design standards and principles,design solutions and wireless network video monitoring system design;On the use of standards,working frequency,network infrastructure, coverage solutions,link budget,frequency planning, equipment power supply,supporting transmission and AC design describes the design proposal,proposed overall solution on wireless mesh network digital video system and applied to practical forest fire prevention wireless network video monitoring system,and achieved practical application of research results.

Video Fire Smoke Detection Based on Static Features of Smoke

Video Fire Smoke Detection Based on Static Features of Smoke

Candidate Smoke Region Segmentation of Fire Video Based on Rough Set Theory

Candidate smoke region segmentation is the key link of smoke video detection; an effective and prompt method of candidate smoke region segmentation plays a significant role in a smoke recognition system. However, the interference of heavy fog and smoke-color moving objects greatly degrades the recognition accuracy. In this paper, a novel method of candidate smoke region segmentation based on rough set theory is presented. First, Kalman filtering is used to update video background in order to exclude the interference of static smoke-color objects, such as blue sky. Second, in RGB color space smoke regions are segmented by defining the upper approximation, lower approximation, and roughness of smoke-color distribution. Finally, in HSV color space small smoke regions are merged by the definition of equivalence relation so as to distinguish smoke images from heavy fog images in terms ofVcomponent value variety from center to edge of smoke region. The experimental results on smoke region segmentation demonstrated the effectiveness and usefulness of the proposed scheme.

Time Frame Selection Based Feature Extraction for Fire Detection in Video Surveillance

This research proposed new simple feature extraction method to characterize the feature of fire that capable to be used in classifying an object as fire or neither in video surveillance for fire detection. The process of extraction feature consists with simple segmentation process in color domain, and the movement. Time frame selection is proposed to select specific video frames that will be extracted and will be placed as key feature or attribute by calculate the number of binary histogram level. We using classification method Back-Propagation Neural Network to classify the features that has been generates and evaluates its accuracy. The result of this experiment has showed the performance of method could give accuracy until 76.67% in classifying video fire detection. ISTIE 2014 and Journal of Computational and Theoretical Nanoscience http://eprints.dinus.ac.id/14130/1/IEEE_Conference_-_Time_Frame_Selection_Based_Feature_Extraction_for_Fire_Detection_in_Video_Surveillance.pdf