EP1256105A2 - Detection de fumee et de flammes - Google Patents

Detection de fumee et de flammes

Info

Publication number
EP1256105A2
EP1256105A2 EP01904091A EP01904091A EP1256105A2 EP 1256105 A2 EP1256105 A2 EP 1256105A2 EP 01904091 A EP01904091 A EP 01904091A EP 01904091 A EP01904091 A EP 01904091A EP 1256105 A2 EP1256105 A2 EP 1256105A2
Authority
EP
European Patent Office
Prior art keywords
flame
smoke
pixels
images
detection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP01904091A
Other languages
German (de)
English (en)
Other versions
EP1256105B1 (fr
Inventor
Michael John c/o Sentec Limited BLACK
Edward Grellier c/o Sentec Limited COLBY
Ian Frederick c/o VSD Limited MOORE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Intelligent Security Ltd
VSD Ltd
Original Assignee
Intelligent Security Ltd
VSD Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GB0002695A external-priority patent/GB0002695D0/en
Priority claimed from GB0010857A external-priority patent/GB0010857D0/en
Priority claimed from PCT/GB2000/003717 external-priority patent/WO2001024131A2/fr
Application filed by Intelligent Security Ltd, VSD Ltd filed Critical Intelligent Security Ltd
Publication of EP1256105A2 publication Critical patent/EP1256105A2/fr
Application granted granted Critical
Publication of EP1256105B1 publication Critical patent/EP1256105B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
    • G08B17/125—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions by using a video camera to detect fire or smoke

Definitions

  • the invention relates to the detection of smoke and flame using image processing technology.
  • CCTV type cameras It is common to install CCTV type cameras in or around buildings, transport facilities such as traffic tunnels, or industrial plants to allow centralised surveillance monitoring. These cameras can be used to detect fires and generate fire alarm information.
  • cameras can be integrated into an automatic fire detection system, which can operate entirely without human intervention thereby reducing the potential for missed alarms.
  • vision systems to detect smoke or flame is known.
  • WO00/23959 VSD Limited.
  • Figure 1 is block diagram showing a smoke and flame detection system
  • FIG 2 is a block diagram showing steps an algorithm used by the system shown in Figure 1;
  • Figure 3 is a block diagram showing another algorithm for use in a flame and smoke detection system.
  • the smoke and flame detection system 10 comprises one or more video cameras 12 which are variously directed at one or more locations which are to be monitored.
  • the cameras also serve a second function as a part of a security or other form of surveilance system, although, it will be understood that one or more cameras could be dedicated solely to fire detection. In the description which follows, purely for the sake of convenience, the system will be described as having one camera 12.
  • the camera is directed at a region to be monitored or view area 14 and outputs a standard 625 line analogue video signal at 25Hz frame rate.
  • a standard video camera from the Hitachi company has proved suitable.
  • Images of the view area 14 captured by the camera are fed to a frame grabber card 16 at a minimum frame rate of 5Hz and preferably approximately 10Hz.
  • the frame grabber card digitises the images to a resolution of 640 pixels per line with 480 lines and feeds the digitised images to a processor 18 at the frame rate.
  • the frame grabber card is a standard piece of hardware and in practice, a National Instruments PCI 1411 device plugged into the PCI bus of a standard PC has proved suitable.
  • the grabber card may utilise Scion Image software.
  • the camera may be a digital camera, in which case the grabber card would not be required to digitise the image. In this case, the grabber card would merely be required to grab digitised images from the camera at the required rate and feed the images to the processor 18.
  • the processor 18 may comprise a standard IBM "" PC using a 750Hz Intel Pentium 3 rM processor with 128 Mb of RAM, although, it will readily be appreciated that this is just one example of many processors, which would be equally suitable.
  • the processor 18 processes the digitised images received from the frame grabber card using separate algorithms 20, 22 for smoke and flame detection. Details of the algorithms are provided below.
  • the processor uses a multi-threaded processing environment, such as windows, to simultaneously run the two algorithms to detect smoke or flame areas within the digitised image.
  • the processor analyses the data produced by the algorithms to assess whether a fire has been detected.
  • the processor may use a vote based analysis to assess whether a fire has been detected. For example, the processor may produce a fire detected signal if there is a yes flame present decision and a yes smoke present decision. This would provide a high level fire present indication. Alternatively, if there is only a yes decision from one algorithm, the processor may produce a lower ranked fire present indication. Yet another alternative would comprise producing a higher ranked fire present indication where both algorithms produce a yes decision and where one of the two, for example, the flame detection algorithm, produces a yes decision while the other produces a no decision and a lower ranked fire present indication where only the other algorithm produces a yes decision.
  • the processor may take the data produced using the algorithms and carry out a statistical analysis to asses whether a fire is detected and that such an analysis may produce an unranked fire detected indication or a ranked fire present indication.
  • a suitable signal is output by the processor using a standard serial RS232 link. The signal can be used to provide an onscreen warning for the operator on a conventional PC screen 28. The operator is then able to make a decision as to whether or not to trigger an alarm or investigate in more detail.
  • the signal may be fed via known interface equipment, such as for example digital to analogue interfaces, to produce an alarm signal using any form of conventional alarm equipment 30.
  • a digital alarm signal could additionally, or alternatively be directed to digital receiving means of the local fire service.
  • the processor may select the destination of the output signal according to the rank (if any) assigned to the fire detected signal. Thus, for example, if the fire detected signal is low ranked, the processor may cause the output signal to be directed to a display and or low level warning device to alert an operator to the possibility of a fire which the operator should then investigate manually. In such a system, the processor may cause the output signal to be directed to a main alarm device if a high ranked fire detected signal is produced. Whilst it is preferred that at some level the processor would cause the output signal to be directed to an alarm device without operator intervention, it will be appreciated that the system could be configured to act simply as an operator warning system, if this is what the user requires.
  • the system includes an operator interface, for example a keyboard and/or mouse 34, to permit an operator to interact with the processing algorithms to customise the system by adjusting parameters employed by the algorithms in order to improve detection performance and/or reduce the incidence of false alarms.
  • the operator interface may also permit the operator to provide commands to the system, for example to terminate an alarm which is deemed a false alarm.
  • the operator may for example adjust the system so that it ignores certain zones in a particular view area or assign differing detection parameters to various portions of the view area.
  • Alternative forms of display and input device for the system would include a touch screen.
  • the system may be provided without an operator input device where it is considered that operator access to the algorithms is unnecessary or undesirable.
  • the system could be configured to receive data and commands from a suitable portable computing device to enable set up and diagnostic work to be carried out by authorised personnel.
  • the system may include an event memory 40, which may be an integral part of the processor or a standalone memory. This event memory could be used to hold images showing the view area 14 at the time a fire detection signal is produced. Such images may be used to assist in fine tuning the system, where for example a significant number of false alarms or operator warnings have been produced, or to provide evidence as to the time, location and/or cause of a particular fire. It will be appreciated that the system may be configured such that an image is recorded in the event memory 40 in response to commands from the processor and/or instructions from the operator.
  • multi-threaded processing allows one software program and one processor to simultaneously process the smoke and fire detection algorithms and also multiple video channels. It will be appreciated that more than one processor may be used to improve processing speed.
  • simultaneous smoke and flame detection improves the ability of the system to provide adequate responses to a detected event whether the event is an instantaneous igntion type fire where there may be little or no smoke or slow progressing fires such as the type that emit smoke. For example, if the system detects smoke without flame in a zone where there is the possibility of a steam leak triggering an initial smoke detection signal, an alarm event can be prevented and/or delayed pending detection of flame. Alternatively, in environments where a flame detection may be triggered without the presence of fire, for example, where conventional sodium lighting is present, an alarm signal may be delayed pending the detection of smoke. Thus the system provides greater flexibility and sensitivity when compared with systems capable of detecting smoke alone or flame alone. It will be appreciated that since the system can simultaneously monitor for the presence of smoke and flame, it can detect all types of fire, whether they be instantaneous ignition type fires or slow burning fires that emit smoke.
  • the fire detection algorithm 20 used by the processor to detect the presence of flame will now be described with reference to Figure 2.
  • the algorithm is coded in a mixture of Lab View rM and Microsoft® Visual ++.
  • the fire detection algorithm comprises a series of steps labelled S 1 to S7.
  • step SI the video image is entered into the algorithm in the form of a monochrome 640 x 480 image where each image pixel has an intensity value of 8 bits resolution.
  • the algorithm processes each pixel individually, using linear mathematical operations.
  • step S2 the monochrome 640 x 480 8 bit image is used to generate two separate averaged 640 x 480 8 bit resolution images which filter out rapidly occurring events, one with filter set at 1.25 Hz and the other with the filter set at 4.0Hz.
  • the absolute difference between pixel values of these two images is then taken to obtain a movement band 640 x 480 8 bit image, which displays entities that are moving in the image within the frequency band between 1.25 and 4.0Hz.
  • This frequency band corresponds with the range of movement frequencies exhibited by petrol flames observed empirically.
  • a dimensionless time constant kl is used to generate a 640 x 480 8 bit image that filters out events that occur more rapidly than 4Hz. kl is calculated according to the relationship:
  • kl is then used to generate an image that filters out events that occur at higher frequencies than 4Hz in the following manner:
  • pMl kl x (live pixel image value) + (value of pMl from previous frame)
  • pMl is a rolling average with a starting value of zero.
  • Each pixel in the 640 x 480 live image has a corresponding value of pMl which can be used to make up the averaged image.
  • a dimensionless time constant k2 is used to generate a 640 x 480 resolution 8 bit image that filters out events that occur more rapidly than 1.25Hz.
  • k2 1/(1.25Hz x time in seconds between successive frames)
  • k2 is then used to generate an image that filters out events that occur at higher frequencies than 1.25Hz in the following manner:
  • pM2 k2 x (live pixel image value) + (I-k2) x (value of pM2 from previous frame)
  • pM2 is a rolling average with a stating value of zero.
  • Each pixel in the 640 x 480 image has corresponding value of pM2 which can be used to make up the averaged image.
  • a so-called movement band 640 x 480 resolution image is generated by taking each of the pixels of these averaged images and calculating the absolute difference between pMl and pM2 by finding the magnitude of the difference between each of the individual pixels obtained by subtracting pMl from pM2.
  • a 640 x 480 image is obtained which only displays events that occur in the frequency band between 1.25 Hz and 4.0 Hz.
  • Each pixel of the movement band image has an 8 bit resolution.
  • step S3 once an image has been filtered using the movement band, the filtered image has a threshold applied to create a map of significant movement in the characteristic frequency band defined by kl and k2.
  • the study of the temporal dynamics of these highlighted pixels is used to decide whether or not flames are present in the video image.
  • the user of the system can set this value to an arbitrary value between 0 and 255 using the graphical user interface provided by LabView I .
  • the threshold map is a Boolean image of 640 x 480 pixels where non- thresholded pixels have a value of zero, and threshold pixels have a value of one.
  • the 'awareness map' is a subset of the 'threshold map'.
  • each pixel in the threshold amp defined in step S3 has an awareness level, which is an indication of the likelihood of the flame existing within that particular pixel. If the awareness level, increases above a user-defined threshold defined as the integer t2 (nominal value of 40), then the threshold pixel is registered with binary value 1, into the awareness map.
  • the awareness map is a 640 x 480 Boolean image.
  • An integer defined as the awareness level is generated for each of the pixels in the awareness map.
  • the value of the awareness level is calculated by comparing successive frames of the awareness map for each of the pixels is equal to zero.
  • a pixel in the awareness map changes from 1 to 0 or changes from 0 to 1 between successive video frames, then 2 is added to the value of the awareness level for that pixel. If pixel in the awareness map does not change (ie stays at 0 or stays at 1) between successive frames, then 1 is subtracted from the awareness level. The minimum value of the awareness level is zero ie if the awareness level becomes negative it is immediately set to zero.
  • step S5 a number of parameters are calculated so that the algorithm can decide whether a flame is present in the video images that are being processed. These parameters may be plotted in a moving graph or used to determine a confidence of a flame detection event.
  • the PlotO parameter is a constant equal to an integer called the Alarm Level, user defined with a default value of 20. A flame is registered in the system when the Plot2 parameter described below exceeds the Alarm Level, which has a nominal value of 20.
  • Low values of Alarm Level mean that the algorithm is fast to react to possible flames in the digitised image, but is susceptible to false detected decisions.
  • High values of Alarm Level mean that the algorithm is insensitive to false flame detected decisions, but is slow to react to possible flames in the digitised image.
  • the Plotl and Plot2 parameters are calculated in the following manner by scanning horizontally across the awareness map. As the scan is performed from left to right across each horizontal line of the awareness map the values of adjacent pixels are compared and a value is entered into an edge counter that starts at a value of zero.
  • Edgesum Sum of horizontal edge transitions in awareness map
  • Pixelsum Total number of pixels with binary value 1 in the awareness map
  • a region of interest is defined by noting the following quantities:
  • the area of the region of interest is defined as:
  • ROIarea (x2 - xl) x (y2- yl)
  • the Plotl parameter is calculated as follows:
  • the Plot2 parameter is calculated as follows:
  • step S6 prior to performing the final flame decision, the 'plot' parameters described above are smoothed using a user defined dimensionless time constant k3 with a time constant of 8.0 seconds.
  • K3 is calculated in the following manner:
  • k3 is applied between successive values of Plotl and Plot2 obtained from successive video images using the same filtering techniques as used by kl and k2 described above. This reduces the noise level in the plotted parameters and reduces the false alarm rate.
  • the decision whether a flame is occurring within the video image has two operator selectable modes; normal mode and tree filter mode
  • Normal flame decision mode is employed when no treelike objects are in the picture.
  • Plotl is ignored.
  • an alarm is triggered when the Plot2 parameter is greater than the user PlotO parameter.
  • the alarm for a flame with the tree filter only occurs when Plot2 is greater than the PlotO and Plotl is greater than zero.
  • the inventors have found that inclusion of the tree filter increases the selectivity of the system, but also increases the amount of time required to reach a decision on whether a flame is present in the picture.
  • the processor includes a comparator, which analyses the differences between different images and the pixels which make up the images. For this purpose, the comparator first compares the image with previous images and by subtraction obtains a signal representative of the difference between successive images.
  • the system also includes an adjustable threshold control level for sensitivity setting and a means by which changes which are representative of signal noise can be eliminated.
  • the output of the comparator is then subjected to the main processing of the signal in accordance with the smoke detection algorithm.
  • the processor is looking to see whether there are changes in the individual pixels of a frame and in the differences between adjacent pixels which would have been caused by smoke particles
  • the processor involves a number of separate analyses and these involve mathematical analysis by appropriate computer software in the signal process as part of the equipment.
  • the signal processing means has to include hardware and/or software to recognise the selected conditions of change so that the presence of a smoke condition can be identified.
  • the analysis can be based on the following:
  • the system has two images to work with, where image is defined as an ordered set of pixels intensities.
  • An image is defined as an ordered set of pixel values where a pixel value is defined as:
  • N is the number of pixels in an image.
  • the system provides two images in order to evaluate the various changes. These images are
  • this change is used to define a sub-set of the images.
  • #S denotes the number of element in the ordered set S and ⁇ S denotes the sum of all elements in the ordered set S.
  • the consistency of the changing area is evaluated over time in order to assess if that area is dynamic in terms of its overall appearance or static. Lighting changes alter the image but the overall appearance does not change.
  • the correlation function is used to evaluate this similarity over time since it is invariant to both scale and gain changes. If an object obscures the background by moving into the area of interest then the appearance within the area of interest will change. If the correlation fluctuates sufficiently over time then the area is deemed to be dynamic. This measure of consistency is forwarded to the decision system.
  • a change in edge information is defined as a change in the value of the edge measure.
  • the edge measure is defined as the sum of the responses of a standard derivative filter kernel where changes have been detected by the previous stage.
  • a standard filter which is employed is the Sobel edge filter. This measure of edge content is forwarded to the decision system.
  • Density is defined as the average number of occupied neighbours for all members of the change set.
  • a "four connectivity" scheme is adapted and consequently the average value of density lies in the range 0 to 4.
  • the aspect ratio is the ratio of the height to the width of the changed region.
  • the smoke detection software is written in C++, compiled using the WATCOM C++ compiler.
  • the features of the software described below are encapsulated in around 50 source code files and a further 50 header files, comprising an estimated 40,000 lines of code in all.
  • the smoke detection algorithm examines, in general terms, the following features of a digitised video stream to determine whether smoke has been detected:
  • Edge information edge definition this may increase or decrease as smoke emerges (depending on what it was like before) Whether the image overall is static or dynamic
  • Zones are rectangular regions selected from the entire image by the user when the system is installed. These would typically be arranged to cover likely areas where smoke might be produced, and (more importantly) not cover problem areas of the scene. Each zone is processed entirely separately, and the outputs from each zone may be combined to generate alarms as required. Pixels in the zone may additionally be eliminated so that they are not included in the calculations - for example, the filament of a light bulb, or a shiny metal object that glints in the sunlight. Again, these are selected by the user when the system is commissioned. At any one time there are two primary sets of image data for the zone - the current image and the reference image. The pixels in these images are denoted by x and x r respectively, in the discussions below.
  • n parameters are calculated. These parameters are formed into an ⁇ -dimensional "vector" , defining a "feature” space.
  • Images are acquired from the grabber card on a regular basis. After any adjustments to normalise the brightness and contrast, the system compares the most recently acquired image (current) with the reference image . If pixels differ by more than an adjustable threshold (camera noise may be taken into account too), then the pixel is deemed to have changed.
  • an adjustable threshold camera noise may be taken into account too
  • the reference image is acquired periodically, when the system has detected no changes in the scene, and when the system determines that the current scene is no longer similar enough to the reference image.
  • This reference image is analysed to generate an "environment mask", using the EDGE algorithm below. This essentially indicates where there is edge detail in the zone.
  • a pixel-by-pixel "filter” mask used in the calculations detailed below, is constructed by combining the changed pixels with the environment mask.
  • the changed pixel mask is only copied to the final filter mask at points where the magnitude of the difference between the current and the reference pixel exceeds the edge detail pixel value. Pixels selected manually as being problematic are also eliminated from this mask at this stage.
  • This parameter counts the number of unmasked pixels in the image that deviate from the mean with the same sign as they do in the reference image.
  • This parameter counts the number of unmasked pixels in the image that deviate from the mean with the opposite sign from the way they do in the reference image.
  • the total number of unmasked pixels in the zone i.e. excluding the masked pixels
  • the edge content algorithm looks at, for each unmasked pixel in the current image, the four adjacent pixels (up/down/left/right). It sums the sum of the magnitude of the differences between the left and right, and between the up and down pixels, for pixels where this exceeds a threshold value set by the user.
  • the correlation function is used as an overall "gate" to the detection process. If this correlation is greater than a preset SIMILARITY, then no further processing is carried out on the zone. This corresponds to the case where the image is essentially the same as the reference image.
  • the masked correlation calculates the same function as the correlation function above, considering only those pixels that are not masked. VARIANCE
  • the pixel values might have a Gaussian distribution about the mean pixel value, or the distribution might be asymmetric or otherwise non-Gaussian.
  • Parameters such as skew, kurtosis and fifth are well known parameters used in statistics to analyse the non-Gaussian nature of distributions. They are calculated as follows:
  • This function looks at the four nearest pixels to each unmasked pixel, and calculates the mean number of these that are unmasked.
  • Opacity is calculated, for the unmasked pixels only, as RUNNING CORRELATION MEAN
  • the unmasked pixels in the current and reference images are examined using the EDGE algorithm above. The routine then calculates the mean ratio of the pixels in the EDGE'd current image and those in the EDGE'd reference image, within the unmasked region, provided that the reference image contained a non-zero value.
  • Rule-based analysis is used initially to determine whether a change in the image has occurred, and whether this change is significant. If it is, then further analysis is carried out to see if the change is considered to be associated with smoke, or whether it is associated with, say, a person walking across the scene.
  • the rule-based analysis uses a scoring system, where points are allocated for each rule which is met. If the points total exceeds a (variable) criteria (typically 90% of the maximum score), the analysis moves to the next level.
  • a (variable) criteria typically 90% of the maximum score
  • the analysis is carried out on a region, which is a subset of the area of the zone, defined by the edges of the unmasked pixels.
  • the "edge-ness" of the region is the ratio of the EDGES to the COUNT of pixels in the image. This is calculated both for the current and the reference image. If the current image edge-ness is outside a preset band, three points are scored. An additional three points are scored if the edge-ness deviates from the reference edge-ness by more than a preset percentage - selectably either up or down.
  • the EDGE_EVIDENCE is decreased by the presence of smoke. If it falls below a preset threshold, three points are scored.
  • the user may determine, when setting up the system, a subset of the available tests to carry out.
  • the maximum score will be less, and the is take into account when determining whether the score has exceeded 90% of the maximum value. If it has, a Bayesian analysis is then carried out.
  • Bayesian analysis provides a well founded decision criteria which takes into account the co-variance of features and provides the ability to discriminate between different classes of event (nuisance and real alarms).
  • An important fact to note when defining features for use with Bayesian analysis is that they should be invariant to external influences such as background and lighting. The algorithm can cope with some variation but in general the effects of external influences should be kept to a minimum.
  • Bayesian statistics are a useful tool in making decisions with multivariate systems such as this.
  • the parameters (MEAN, TOWARDS_COMMON_MEAN etc) are combined together into an ⁇ -dimensional vector. These vectors are used to "train” the system by building up a set of statistics. More specifically, the system stores data for nuisance and real alarms as separate classes. For an ⁇ -dimensional vector v the sums s and S are calculated for N different alarm events as follows, separately for nuisance and real alarms.
  • the Bayesian decision function takes a vector, v, from the current zone/region, and calculates a real decision value, d, as follows: 5
  • d is calculated against the two reference classes - nuisance and real, giving d n and d r If d r is greater than d n , the Bayesian analysis signals an alarm condition.
  • an important feature of the algorithm is to combine a rule-based analysis with a statistically based analysis, and particularly with one based on Bayesian analysis.
  • the rule based analysis takes place first and if certain criteria are met then the Bayesian analysis is instigated.
  • the Bayesian analysis and the rule-based analysis disagree.
  • the confidence in the Bayesian analysis is used to determine whether the alarm is real or nuisance.
  • the difference between real and nuisance is based on experience and the system builds up in accuracy over time.
  • the reference image is updated. This effectively adjusts for changes in, for example, lighting level.
  • the Bayesian analysis is important in avoiding false alarms, it is envisaged that in certain circumstances the smoke detection algorithm may omit a second level of analysis and instead rely on the output of the flame detection algorithm as a means for reducing the incidence of false alarms.
  • a fire detection algorithm 100 is shown in Figure 3.
  • the algorithm is specified in general terms and some steps may utilise one or more steps either or both of the flame detection algorithm and the smoke detection algorithm 20 described above.
  • the individual steps which comprise the algorithm 100 are indicated in Figure 3.
  • the algorithm analyses moving components in the images received by the processor by examining the difference between successive images or a current image and a reference image which depicts a no-fire event condition. The total perimeter area, position and density of the resulting patterns can then be combined with one another to generate quantitative estimates of certain fire attributes in order to produce a flame detected decision. To first order, it is possible to obtain an estimate of the probability of flame occurring by adding these estimates, or parameters, together for each difference frame.
  • a list of fire attributes which video images of fires possess and can be used to determine whether a fire is occurring within an image comprises:
  • the fire detection algorithm may use a colour camera and the algorithm includes the step of determining whether the image to be processed is from a colour camera. If it is, a colour filter can take information from the red, green and blue channels to see if the image includes the spectral characteristics of a blackbody radiating between 2000K and 3000K. Since CCTV are also sensitive to near IR( ⁇ 900nm), this information can also be gathered for comparison with a suitable filter.
  • the rule application function 50 applies a linear combination of statistical parameters. To first order determination, a sum of area, perimeter and number of moving particles is used.
  • Various aspects of the invention may include : A flame detection algorithm that processes a sequence of video images to detect sequences of images of flames;
  • a system implementing the flame detection algorithm comprising a video source, a frame grabber and a processor and means to trigger an external alarm when flame is detected;
  • An algorithm that classifies changes in a sequence of images between flicker like behaviour and non-flicker like behaviour An algorithm comprising filters yielding a binary image of areas of flame like behaviour in a sequence of images;
  • a further algorithm that determines on the basis of values returned by such an algorithm whether or not to sound an alarm
  • An algorithm which successively uses one or more of the above recited algorithms to generate parameters which can be used to decide whether a flame is occurring in a picture and can differentiate between trees moving in the wind and flames.
  • the smoke detection algorithm alone or in combination with the flame detection algorithm can be used in a gas detection system.
  • steam could be detected.
  • Steam would initially be detected as a presence similar to smoke.
  • steam will tend to evaporate and should fail to be detected in subsequent detection sequences.
  • a relatively short duration of yes smoke detected indications could indicate a detection of steam rather than smoke.
  • a no flame detected indication in combination with smoke detected could also be indicative of gas rather than smoke/fire detection.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Fire-Detection Mechanisms (AREA)

Abstract

L'invention concerne un procédé destiné à actionner un ordinateur de détection de fumée et de flammes qui consiste : à recevoir des images numérisées d'une région à surveiller, à comparer les pixels d'une de ces images avec les pixels d'une autre image selon deux procédures prédéterminées, afin de produire une décision de présence de flammes et une décision de présence de fumée, et à fournir un signal d'incendie détecté en fonction des décisions de présence de fumée et de flammes.
EP01904091A 2000-02-07 2001-02-07 Detection de fumee et de flammes Expired - Lifetime EP1256105B1 (fr)

Applications Claiming Priority (7)

Application Number Priority Date Filing Date Title
GB0002695A GB0002695D0 (en) 2000-02-07 2000-02-07 Video fire detection
GB0002695 2000-02-07
GB0010857 2000-05-05
GB0010857A GB0010857D0 (en) 2000-05-05 2000-05-05 Smoke & flame video detection system
PCT/GB2000/003717 WO2001024131A2 (fr) 1999-09-27 2000-09-27 Algorithme de detection d'incendie
WOPCT/GB00/03717 2000-09-27
PCT/GB2001/000482 WO2001057819A2 (fr) 2000-02-07 2001-02-07 Detection de fumee et de flammes

Publications (2)

Publication Number Publication Date
EP1256105A2 true EP1256105A2 (fr) 2002-11-13
EP1256105B1 EP1256105B1 (fr) 2006-09-20

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EP01904091A Expired - Lifetime EP1256105B1 (fr) 2000-02-07 2001-02-07 Detection de fumee et de flammes

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EP (1) EP1256105B1 (fr)
DE (1) DE60123214T2 (fr)
WO (1) WO2001057819A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1496483A1 (fr) * 2003-07-11 2005-01-12 Siemens Building Technologies AG Mèthode et dispositif de détection de flammes
EP4174813A3 (fr) * 2017-03-20 2023-07-05 Oy Halton Group Ltd. Méthodes et systèmes de dispositifs de sécurité incendie

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EP4174813A3 (fr) * 2017-03-20 2023-07-05 Oy Halton Group Ltd. Méthodes et systèmes de dispositifs de sécurité incendie

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WO2001057819A8 (fr) 2002-10-24
WO2001057819A2 (fr) 2001-08-09
DE60123214T2 (de) 2007-09-20
WO2001057819A3 (fr) 2002-07-18
DE60123214D1 (de) 2006-11-02
EP1256105B1 (fr) 2006-09-20

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