CN1190759C - Video Smoke detection system - Google Patents
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- CN1190759C CN1190759C CNB018057217A CN01805721A CN1190759C CN 1190759 C CN1190759 C CN 1190759C CN B018057217 A CNB018057217 A CN B018057217A CN 01805721 A CN01805721 A CN 01805721A CN 1190759 C CN1190759 C CN 1190759C
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- 238000001514 detection method Methods 0.000 title claims abstract description 85
- 239000000779 smoke Substances 0.000 title claims abstract description 66
- 230000033001 locomotion Effects 0.000 claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000000605 extraction Methods 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims description 17
- 229920006395 saturated elastomer Polymers 0.000 claims description 13
- 230000008859 change Effects 0.000 claims description 8
- 238000011160 research Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 11
- 230000002123 temporal effect Effects 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000012544 monitoring process Methods 0.000 description 8
- 230000008901 benefit Effects 0.000 description 4
- 238000005286 illumination Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 241000202252 Cerberus Species 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000003595 mist Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000013517 stratification Methods 0.000 description 1
- 230000036413 temperature sense Effects 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
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- 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
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Abstract
The invention relates to a video smoke detection system comprising at least one device for recording video images (A) and one signal processing step, wherein the illuminosity of a single pixel or a group of pixels in the video images (A) is determined. The illuminosity of the pixel is determined by means of a process wherein a value representing the illuminosity is obtained. The temporal progression of the above-mentioned value is examined for characteristic changes in the presence of smoke. Determination of the illuminosity of the pixel occurs by means of an edge extraction process (5), whereby an edge value is associated with each pixel. The edge extraction process (5) also takes place after examination of the video images (A) for movement, designated as movement detection (7). Determination of the edge values and movement detection (7) occur by means of digital images (6, 8) which are continuously updated by a hysteresis algorithm.
Description
Technical field
The present invention is positioned at the field of carrying out Smoke Detection by video image.In dwelling house and industrial building, storehouse, museum, church and resemble construction,, for example carry out Smoke Detection based on the smoke alarm that causes light diffusion or light reduction principle by smog with on the ceiling that is installed in space separately.In railway or vcehicular tunnel, in fact do not adopt smoke alarm on the contrary, because here because the air movement and the air stratification that cause by traveling automobile and train do not guarantee that the smog that is produced can arrive the smoke alarm that is installed on the ceiling with the available time limit when fire alarm.So adopt for the monitoring of the fire alarm in the tunnel and for example resemble Siemens Construction Technology GmbH, the so-called Linear Temperature sense warning system that the fibre laser system of Cerberus portion is such today.
Background technology
Recently, the ongoing effort video system that will certainly exist for the traffic monitoring in the tunnel is used for Smoke Detection.Because video image very often is unessential for the observer, only causes very little change in the video image by smog in addition, thereby can not consider on screen, to monitor by personnel.If situation is like this really, then can only monitor by the automatic analysis of video image.In a kind of known method of the video image of analyzing and researching automatically aspect being used for occurring, each intensity values of pixels of the successive images that is compared to each other from smog.Representative is a kind of to exist the intensity level of caused brighter image by smog if measure, and then infers the existence of smog, and starts and report to the police.
In addition, the problem that exists in the method is, do not realize the smog before the bright background, and detect less than in addition only generate the fire of low smoke.In addition, the brightness that for example causes by the personnel because of the camera coverage of passing by changes and may start false alarm.Attempt as follows addressing this is that, the scope of the outside of promptly except that original monitoring range, also analyzing and researching, and just interrupt the observation of monitoring range when in this outside scope, changing.The shortcoming that this method has is that fire just is detected after certain postpones sometimes, and does not realize the mist source in the foreign range of arranging being additional to monitoring range.
Summary of the invention
The present invention relates to a kind of that have at least one device that is used to absorb video image and have a kind of video smoke detection system of signal Processing level of determining each pixel of video image therein or respectively organizing the brightness of pixel.
The task that should solve with the present invention is, a kind of Smoke Detection rapidly and reliably of realizing is described, and is particularly suitable for the video smoke detection system that uses in highway and railway tunnel.Should carry out described Smoke Detection in the stage as far as possible early that fire alarm forms, and should in fact get rid of false alarm.
According to video smoke detection system of the present invention, having at least one is used to absorb the device of video image and has a kind of therein by handling the signal processor of determining each pixel of video image or respectively organizing pixel intensity, in described processing, obtain a kind of for the representational value of described brightness, and the variation that occurs at sign smog comes the time course of described value is analyzed and researched, described processing is that a kind of edge extraction that distributes a rim value for each pixel handles, carry out the comparison of described rim value and a mean value for each pixel, and obtain the counter image of an explanation rim value in from then on comparing with respect to the time response of described mean value.
Smoke detection system of the present invention is based on following understanding, i.e. the appearance of smog causes the reduction of contrast.Make described edge fog when determining pixel intensity handling, or these edges will disappear by a kind of edge extraction.At this moment, the advantage of this process is that rim value is insensitive with respect to the illumination change of integral body.
Especially each relatively time the at rim value and described mean value upgrades described counter image, this counter picture specification the brightness of relevant pixel how once on average to be positioned on the described mean value continually on certain time period.
Described counter image and a kind of threshold value are compared, and when surpassing this threshold value, an initialization value is added up to a currency.
The 3rd preferred implementing form of video smoke detection system of the present invention is characterised in that, also carries out a kind of analysis and research hereinafter referred to as motion detection from the motion aspect to described video image except that the edge extracts processing.
At first arranging in the highway and railway tunnel of system of the present invention for its monitoring, almost only is that what to produce the edge by the mobile object between relative edges and the video camera is arranged is not by the caused covering of smog.Because such object is not to embody suddenly, cover on the position at described edge but moved on to them usually, so can be from following situation, promptly under the situation that is not the edges cover that caused by smog, the object that covers described edge must just produce motion before this.Therefore described motion detection provides a kind of and has been used to distinguish those edges of being covered by object and by the reliable criterion at the edge that smog covered.
Especially the counter image that upgrades by a kind of hysteresis algorithm of continuous usefulness had both carried out the described definite of rim value, also carried out described motion detection.Especially a kind of algorithm based on the standardization simple crosscorrelation is used for described motion detection.
Described hysteresis algorithm especially has a minimum value and a maximal value, and two therebetween threshold values, wherein, described counter image just skips on the maximal value when surpassing lower threshold value when upwards counting, and just skips on the minimum value when being lower than upper threshold value when counting downwards.
This hysteresis algorithm has realized noisy image is used for detection algorithm.Under the situation of the hysteresis of suitable parameterization, the edge that causes by noise in the counter image, do not occur, and edge can single there be not noise image and disappear because of one.
The 4th preferred implementing form of video smoke detection system of the present invention is characterised in that, adopt three kinds of data structures, a kind of have data segment about the information that is present in the edge in the image separately, a kind of have be used for eliminating for Smoke Detection do not need the data segment of bit mask of the image range considered and observed image itself, wherein, described edge and image remain unchanged between the subsequent iteration of processing procedure, and reinitialize described bit mask for each iteration.
The 5th preferred implementing form of video smoke detection system of the present invention is characterised in that, analyzes described image and edge by pixel ground, and for the analysis of carrying out bit mask hereinafter referred to as the group of a plurality of pixels of piece.
The 6th preferred implementing form of video smoke detection system of the present invention is characterised in that, on two paths, that promptly be used for calculating the edge that is present in image and be used to upgrade first path of the data on the edge Already at one, and a processing that is used to set up the enterprising line data in second path of bit mask, wherein, this second path comprises described motion detection.
The 7th preferred implementing form by video smoke detection system of the present invention, second path comprises saturated to the described device that is used to absorb video image of described block check in addition, wherein with some pieces of saturated pixel mark of ascertaining the number, and do not consider these pieces for the analysis of the counter image at edge.
Another preferred implementing form of video smoke detection system of the present invention is characterised in that, image segments arbitrarily can be got rid of outside analyzing by a kind of shielding.Especially will be used for by the bit mask that described motion detection and saturated check are set up, be updated in counter image used when eliminating the image range that to consider for Smoke Detection.
Another preferred implementing form of video smoke detection system of the present invention is characterised in that, carries out a kind of check before making about the judgement that exists smog, whether exists the edge of enough numbers for this judgement.
Description of drawings
Below by embodiment and accompanying drawing in detail the present invention is described in detail; Showed:
Fig. 1 is the block scheme of video smoke detection system of the present invention,
Fig. 2-4 is each process flow diagram of the function of first embodiment that is used to set forth video smoke detection system of the present invention; With
Fig. 5 is a process flow diagram of functional mode that is used to set forth second embodiment of video smoke detection system of the present invention.
Embodiment
Press accompanying drawing 1, video smoke detection system of the present invention is made up of a plurality of video cameras 1 and a common processor 2 that carries out video camera 1 Signal Processing and analysis basically.Described video camera 1 for example is installed in the vcehicular tunnel, and is used for traffic monitoring, for example is used for observing and being used to detect dust formation, accident and similar thing of monitoring and controlling traffic rule.Described video camera is to link to each other with the people operational centre on duty that has of observing traffic conditions in the tunnel through monitor.Arranged described processor 2 dispersedly, wherein, common processor 2 is distributed to certain number, for example 8 to 10 video cameras respectively.
Be broken down into pixel at the video image described in the processor 2, give the set of dispense brightness value of each pixel and/or these pixels, and by the brightness value of described pixel and relatively carrying out about there being the judgement of smog of a reference value.Importantly, this distribution does not rely on whole brightness and changes, and does not just rely on the variation of entire image illumination when distributing brightness value for each pixel or pixel groups.Can reach this independence as follows, promptly the rim value of a kind of deduction of real expression be distributed to described pixel illumination.The identification of smog on the basis of this assumption, promptly described edge is weakened by smog, or disappears.
Signal Processing in the processor 2 and signal analysis further can be divided into two function square frames of representing with pixel intensity 3 and Smoke Detection 4 in accompanying drawing 1.By this division, the process flow diagram of accompanying drawing 2 showed for the asking for of the representational value of pixel intensity (pixel intensity 3), and that process flow diagram of accompanying drawing 3 has then been showed from smog and existed the aspect that these values are made further to analyze and research (Smoke Detection 4).Accompanying drawing 4 has been showed by the described method of accompanying drawing 2 for the necessary additional step of some purposes (Smoke Detection in the inner space that for example resembles passageway, entrance hall and similarity).
To be decomposed into pixel by the video image of each video camera 1 picked-up, and digitizing, thus for each pixel with coordinate i and j determine this pixel such as can be at the intensity level I between 0 and 255
I, jFrom described intensity level I
I, jIn form mean value M for certain group of 3 * 3 or 5 * 5 pixels for example
I, jOr intermediate value or by value that low-pass filtering obtained.The advantage that described intermediate value has is can be with 8 calculating of carrying out it.
Be parallel to the calculating of mean value or intermediate value, from described intensity I
I, jRim value of middle acquisition, this is by inferring or realizing by frequency analysis (high-pass filtering, such as wavelet transform).For example can obtain the rim value K of each pixel by using Robert arithmetical unit or Sobel arithmetical unit
I, jBut can certainly adopt more complex calculations device for edge calculations, and be applied to such as resemble on the big zone of 5 * 5 or 7 * 7 pixels.
Analysis and research then, described rim value K
I, jWhether be positioned on mean value or the intermediate value.If sure, then to a value Z
I, jAdd meter one number δ
Ob, and by the new old value Z of value replacement
I, j, if negate, then from a value Z
I, jDeduct a number δ
Un, and by the new old value Z of value replacement
I, jDescribed value Z
I, jBe a following number, it has illustrated the brightness of described rim value and relevant pixel is how to be positioned at certain threshold value (mean value or intermediate value M continually on certain time period fifty-fifty
I, j) on.This number Z
I, jHereinafter referred to as the counter image.Z
I, jThe value scope be 0 to 255 for example, Z
I, jInitial value be 0 when system initialization.Described several δ
UnAnd δ
ObCan be identical or different, such as both can equal 1.
In view of the influence of motion to rim value, described counter image Z
I, jHas a kind of special advantage.If an object moves through described image, then at least one edge also moves through this image, and this causes at the edge separately, and the pixel on the place has a higher rim value, described counter image Z
I, jTherefore δ rises.In case described edge has left relevant pixel, described counter image Z
I, jThen reduce δ
Un, make the edge pass video image on the whole to the counter image Z of each pixel
I, jThere is not any influence.
The last counter image Z that is obtained
I, jSo especially a kind of representational value of brightness for relevant pixel.At analysis and research counter image Z
I, jThree kinds of time scales of Shi Caiyong: the frequency of the video image that absorbs, for example 1/25 second, 255 images after per 10 seconds and such as per half an hour.
Press accompanying drawing 3 with described counter image Z
I, jWith a threshold value S
zCompare.If described counter image Z
I, jBe positioned at described threshold value S
zUnder, then do not carry out any running, if it is positioned at described threshold value S
zOn, then carry out addition, promptly be worth ∑
xImprove 1, and new thus value replaces.So obtain described initialization value ∑
x 0, make when initialization with ∑=0 beginning and add up, wherein, a kind of initialization value ∑ that is used as then appearred after certain stabilization sub stage in several seconds
x 0Stationary value.∑
xUnder normal situation, should equal ∑
x 0
If ∑
xObviously greater than ∑
x 0, new edge then appears, and this may following reason cause that a static object is arranged in the image range of video camera.Object such in the tunnel may be a static automobile for example, or may be a kind of object that is parked in this passageway in the passageway; Covered certain image range by described object in both cases, this is called covering in accompanying drawing 3.Under situation about covering, redefine the initialization value ∑
x 0Form merchant's ∑ subsequently
x/ ∑
x 0, and with smog threshold value S
RCompare.If described merchant is positioned under the smog threshold value, and therefore the edge is weakened or is disappeared, and then starts and reports to the police.
As long as edge translation in prospect clearly, this is always this usually situation in the tunnel, then for the accurate and reliable not Smoke Detection of false alarm, merchant's ∑
x/ ∑
x 0With smog threshold value S
RRelatively be absolute enough.For the Smoke Detection in highway and the railway tunnel, thereby adopt a kind of functional system shown in accompanying drawing 2 and 3 that has.
During Smoke Detection in relating to the inner space that the people stays, situation then is very.Confirm, stand on the three unities and mutually the people of talk implement a kind of round-robin or back and forth motion on described place, different with translation motion is that this motion no longer drops on described counter image Z
I, jOutside.The motion of reticulate pattern or pattern also is debatable.These motions cause producing new edge, and this may compensate because the reduction or the minimizing at the edge that smog causes make and can not detect smog reliably more sometimes.In general, motion causes new edge usually and also covers the edge sometimes, and smog is not directed at new edge, but the reduction edge.The smog that may be directed at new edge sometimes on remote is a kind of exception of this rule.Owing to the part that is arranged in the top of video image from video camera scope farthest, so all can get rid of this effect by the image section that shields this top, maybe can take following hypothesis, promptly the edge that is formed by smog only moves very lentamente.
For the disturbing effect that prevents to move, adopt when needed shown in the accompanying drawing 4, be used to eliminate motion and from edge K
I, jThe subroutine that (accompanying drawing 2) sets out.In principle also can be from described intensity I
I, jSet out, but this is with the shortcoming that exists interference direct current composition.Form the difference DELTA K of successive images
I, jAnd with this difference DELTA K
I, jWith a kind of movement threshold S
BCompare.If Δ K
I, jBe positioned under this threshold value, then do not have motion.At Δ K
I, j＞S
BThe time, the pixel that satisfies this condition is aggregated into the subregion that therefrom shields described motion.Realize the latter as follows, promptly do not upgrade described counter image Z
I, j, and the last counter image before the described motion is used for described subregion.
By a kind of morphologic wave filter (erosion) erasure signal noise.This means following content: it is a kind of bianry image that the error image that changes number of pixels in the described subregion is provided.Run over this bianry image with a kind of pattern, and give the pixel that covers with described pattern with value " 1 ".So point out the end of described motion, make described subregion from described image, disappear in succession, and reduce the edge.
Accompanying drawing 5 has been showed the process flow diagram of second embodiment of video smoke detection system of the present invention, and the characteristics of this video smoke detection system especially are the height reliability of jamproof height robustness and Smoke Detection.Represent observed image with reference symbol A in the accompanying drawing 5.
Below introduce several general elaborations before the described process flow diagram in explanation: because described edge not only may be covered by smog, and may be by at video camera with there is the object between the relative edges to cover, so additionally from the observed image of motion aspect analysis and research.At this moment, starting point is, an object that covers the edge does not suddenly produce on this position, goes but moved to there.
When Smoke Detection, the on the other hand differing time frames that note, that will consider on the one hand and that point that should differentiate mutually on the other hand.For example as the DE Camera Shake that is caused by the truck that closely crosses, exist the effect very rapidly in the submicrosecond scope, this can eliminate by forming unsteady mean value.Also have for example to resemble and cause by smog, medium effect rapidly in 10 seconds scopes because described smog needs about 10 seconds, arrives its detected place up to it, and effect slowly in 10 minutes scopes, perhaps slower effect arranged for example.The latter for example is the obviously mobile influence that is produced by the sun.A kind of possibility that be used for distinguishing these time frames and that be used to be identified in the effect of correct time frame is the counter image with hysteresis.
A counter image is a series of value, is generally the size of an image that can amplify or dwindle.These values are generally used for for example counting of incident.The not only rim detection of algorithm shown in the process flow diagram, and Smoke Detection all depends on the counter image that upgrades with a kind of hysteresis algorithm.Described hysteresis is characterised in that four values, and is minimum, low, high and the highest, and wherein, " minimum " and " the highest " forms the counter limits that can not be lower than or exceed.Described value " low " is positioned on the described value " minimum ", and described value " height " is positioned between described value " low " and " the highest ".If meter reading is positioned between " minimum " and " low ", or be positioned between " height " and " the highest ", then normal fully counting, promptly meter reading improves 1 to the incident that each detected.When meter reading reached value " low " and detect other a incident, it then jumped to the highest.Similarly, when meter reading reduces when reaching described value " height " from the top down, meter reading then jumps to minimum.
This hysteresis mechanisms makes can use noisy image for detection algorithm.Under the situation of the hysteresis of suitable parameterization, the edge that causes by noise in the counter image, will not occur, and the edge can be owing to a single noisy image disappears.In addition, also be suitable for following relation: the number of the single image that described value " " continues, on these single images, must there be a kind of feature that is used to detect or incident, such as edge, and gone out the number of following single image in succession in described value " ", promptly after these single images, described incident disappears under the situation that Counter Value reduces.Because certain time interval is equivalent to this number of single image respectively, so these time intervals then are yardsticks in the reaction time of described algorithm.
Analysis shown in the process flow diagram is from adopting a kind of such as based on the rim detection 5 of the method for Sobel arithmetical unit.The brightness of each pixel of described each single image of Algorithm Analysis, and follow the tracks of live history by the counter image 6 that the above-mentioned hysteresis mechanisms of usefulness is upgraded.At this moment, the environment for each pixel calculates two values:
With a kind of value of providing q
SobelThe Sobel edge detection filter be applied in the described environment;
Pixel for described environment is calculated a mean value q
Sum
Then by adopting more described two values of two scalar factor (DiffFac and SumFac):
f
DiffFac·q
sobel<？>f
sumFac·q
sum
When the left side of this inequality during greater than the right side, then improve counter 6, when being not more than, then reduce it.Use described hysteresis mechanisms in both cases.
Be parallel to rim detection 5 and carry out a kind of motion detection 7, for example employing is based on the algorithm of standardization simple crosscorrelation to it, and described algorithm is roughly carried out as follows:
Described standardization simple crosscorrelation is:
(formula 1)
At time t, the image that takes out 4 * 4 pixels for example and is seen these pixels as vector among a small circle
Use vector
The same scope of the next image when being illustrated in time t+1.If described scope did not change, then at all
1 Shang Zeyou value 1 by formula.Otherwise the variation in described scope can change described merchant, the feasible yardstick that the degree of this change can be adopted as the change intensity in the described scope.
For adaptive, in formula 1, take advantage of molecule and denominator, and be similar to the standardization simple crosscorrelation of formula 1 and write down the product of formation like this with the factor with the processor that is adopted.If molecule is less, then carried out motion, and the corresponding scope of mark.The unexpected variation of light situation or lighting condition influences the both sides of inequality about equally consumingly, makes that described motion detection is impregnable with respect to the unified variation in the image.Obtain a present image map in this way with 4 * 4 block of pixels.
Next procedure is to calculate the piece that should not consider when analyzing the counter image 6 at edge.Certain number in should detected image, all pieces of the incident that taken place therein of 4 * 4 pixels for example, the described Smoke Detection algorithm of these incident negative effects.These pieces generate a kind of as the complete image size 1/16 counter image and the bit mask 8 that shows.Determine described size by observed piece when the motion detection, the size of this piece can change again.
The next stage is revise video sensor saturated.Thisly saturatedly can bring different problems:
Have only not only unsaturated but also when being not complete black, described standardized simple crosscorrelation just works when the pixel of image;
The boundary of saturated fully image segments occurs as the edge.As if the unexpected variation of illumination can give this impression, moved relevant edge and also can disappear subsequently.
In the scope of saturated pixel, there is not the edge.When relevant scope arrives when saturated, the edge of originally having discerned disappears.
For this reason, in saturated check 9 for each scope by with relatively the checking an of ultimate value, whether the pixel of certain number is saturated.If sure, the then same relevant piece of mark.For fear of the object that moves have " hole "-since described motion detection only detect the part of mobile object-, may be applied in the described bit mask 8 by the porose extended arithmetic device 10 of filling a kind of.
In the described stage, the bit mask 8 of being calculated in motion detection 7, saturated check 9 and the extended arithmetic device 10 is used to the counter image of replacement and obsoleteness process 11 (eliminating the image range that needn't consider for Smoke Detection) now, wherein, use the hysteresis mechanisms that has illustrated again.
On this aspect of algorithm, have two kinds of counter images, also promptly: be detected the counter image 6 that has upgraded of all pixels at edge and the counter image 11 that has upgraded of all pieces that will eliminate.Now the latter's counter image and a parameter are used for so changing described counter image 6 jointly, feasiblely not assessed by any reliable edge that may disturb the effects of Smoke Detection to become possibility.Each piece in the counter image 8 and a threshold value are compared.In the time of on described value is positioned at this threshold value, all pixels in the counter image 6 are set on the minimum value.From the counter image 6 at edge, calculate two kinds of amounts of representing different number of edges constantly now.Number of pixels in the edge that first kind of amount is the present existence on first threshold.Second kind of amount is the number of pixels on second threshold value, wherein, and the number of pixels in the edge that this second threshold value can be interpreted as roughly existing in the moment in a past.
In order to calculate these two kinds a kind of functions of amount definition
This function is counted the pixel C in the image of counter with a value on the threshold value l
I, jNumber.Can calculate described two kinds of amounts now with this function, this moment, estimation like this was positioned at the number of a pixel on the edge now, made l very near-earth is changed to described pixel C
I, jThe maximal value W that can reach
mOn.In order to consider the noise in the image A, for the common selective value (W of l
m-k), wherein, k means the number of frame, and for example being generally on the fixing video camera in the tunnel is roughly 250.
Can add a parameters " picture altitude to the subroutine that pixel counts is used ", this parameter is impelled the upper part of the image of only considering that Smoke Detection is used, such as the first half.Because smog upwards rises usually, this is rational.Additionally can be outside analyzing with a kind of shielding image segments eliminating arbitrarily.
Before judging whether smog exists, whether check exists enough edges in a step 12 now, so that can make this judgement.This check directly is parked in the video camera front for big truck for example, and the situation that described image does not have any edge is necessary.Owing to can not detect fire in this case, indicate that described algorithm can not workable undesired signal under present case so should start one.In order to postpone other action in short-term, and for more insensitive, and adopt one can or to be zero for smog, or greater than zero interrupting value.Before soon, detected in the latter case and do not had enough edges.
Be less than the number of pixels that is equivalent to the edge minimum number if exist, then can carry out two kinds of actions: if described interrupting value has been not equal to zero, then reduce it, and if it reach 1, then start undesired signal.On the contrary, if described interrupting value is zero, then it is brought up to value greater than zero.If exist abundant edge,, and proceed to handle then with described interrupting value set to zero.
If the reliable detection that exists for smog is enough number of edges, then average summation and the difference by the edge carried out about there being the judgement of smog in the stage 13.Described difference and a parameter multiply each other, and compare with described summation.If described summation is bigger, then there is not smog; Report to the police otherwise start.Stop the processing of present image in both cases, and begin next treatment of picture.
Such as the startup of can reporting to the police as follows, promptly connecting relevant video camera, show a kind of corresponding warning in alarm center that someone is on duty or the Surveillance center, this warning impels operating personnel that the image that is provided by relevant video camera more is provided with eyes.Described center for example can be a kind of police service center or the fire-fighting center in the base in city or area, perhaps also can be the command centre of vcehicular tunnel.
Claims (17)
1. video smoke detection system, have at least one device that is used to absorb video image (1) and have a kind of therein by handling the signal processor (2) of determining each pixel of video image or respectively organizing pixel intensity, in described processing, obtain a kind of for the representational value of described brightness, and come the time course of described value is analyzed and researched at characterizing the variation that smog occurs, described processing is that a kind of each pixel of giving is distributed a rim value (K
I, j) edge extraction handle (5), it is characterized in that: carry out described rim value (K for each pixel
I, j) and a mean value (M
I, j) comparison, and from then on obtain an explanation rim value (K in relatively
I, j) with respect to described mean value (M
I, j) the counter image (Z of time response
I, j, 6).
2. by the smoke detection system of claim 1, it is characterized in that,, especially carry out the described of pixel intensity and determine, wherein, distribute a rim value (K who determines by high-pass filtering for each pixel by a kind of wavelet analysis by a kind of frequency analysis
I, j).
3. by the smoke detection system of claim 1, it is characterized in that, at rim value (K
I, j) and described mean value (M
I, j) each relatively the time, upgrade described counter image (Z
I, j, 6), this counter image (Z
I, j, 6) and illustrated the brightness of relevant pixel is how on average to be positioned at described mean value (M continually on certain time period
I, j) on.
4. by the smoke detection system of claim 3, it is characterized in that, described counter image (Z
I, j, 6) and a kind of threshold value (S
z) compare, and surpassing this threshold value (S
z) time with an initialization value (∑
x 0) add up to a currency.
5. by the smoke detection system of one of claim 1 to 4, it is characterized in that, the video image (A) that is absorbed is analyzed and researched from the appearance aspect at new edge, wherein, by last spaced counter image (Z of a kind of time
I, j, 6) correlation computations infer the existence at new edge, and when existing new edge, carry out initialization value (∑
x 0) a kind of redefining.
6. by the smoke detection system of claim 4, it is characterized in that, by described currency (∑
x) and initialization value (∑
x 0) form a merchant, and with this merchant and a smog threshold value (S
R) compare, and the startup of when surpassing the latter, reporting to the police.
7. press the smoke detection system of claim 1 or 2, it is characterized in that, except that extracting to handle (5), the edge also carries out a kind of analysis and research hereinafter referred to as motion detection (7) from the motion aspect to described video image (A), the counter image (6 that upgrades by a kind of hysteresis algorithm of continuous usefulness wherein, 11) both carry out the described of rim value and determined, also carried out described motion detection (7).
8. press the smoke detection system of claim 7, it is characterized in that, described hysteresis algorithm has a minimum value and a maximal value, and two therebetween threshold values, wherein, described counter image just skips on the maximal value when being lower than lower threshold value when upwards counting, and just skips on the minimum value when being lower than upper threshold value when counting downwards.
9. by the smoke detection system of claim 8, it is characterized in that, a kind of algorithm based on the standardization simple crosscorrelation is used for described motion detection (7).
10. press the smoke detection system of claim 8, it is characterized in that, adopt three kinds of data structures, a kind of have data segment about the information that is present in the edge in the image separately, a kind of have be used for eliminating for Smoke Detection do not need the data segment of bit mask (8) of the image range considered and observed image itself, wherein, described edge and image remain unchanged between the subsequent iteration of processing procedure, and reinitialize described bit mask (8) for each iteration.
11., it is characterized in that, analyze described image and edge by pixel ground, and carry out the analysis of bit mask (8) for group hereinafter referred to as a plurality of pixels of piece by the smoke detection system of claim 10.
12. smoke detection system by claim 10, it is characterized in that, on two paths, that promptly be used for calculating the edge that is present in image and be used to upgrade first path of the data on the edge Already at one, and a processing that is used to set up the enterprising line data in second path of bit mask (8), wherein, this second path comprises described motion detection (7).
13. smoke detection system by claim 12, it is characterized in that, second path also comprises saturated to the described device that is used to absorb video image of described block check, do not consider these pieces wherein with some pieces of saturated pixel mark that ascertain the number, and for the analysis of described counter image (6).
14. the smoke detection system by claim 13 is characterized in that, can be with image segments eliminating arbitrarily outside analyzing by a kind of shielding.
15. smoke detection system by claim 14, it is characterized in that, described bit mask (8) of being set up by motion detection (7) and saturated check (9) is used to, and is updated in counter image (11) used when eliminating the image range that needn't consider for Smoke Detection (13).
16. smoke detection system by claim 15, it is characterized in that, by described when eliminating the image range that needn't consider for Smoke Detection (13) used counter image (11) so change the counter image (6) at edge, making the edge assess with respect to the factor of disturbing described Smoke Detection (13) sometimes is impregnable to a great extent.
17., it is characterized in that by the smoke detection system of claim 16, before making, carry out a kind of check (12) about the judgement that exists smog, whether exist the edge of enough numbers for this judgement.
Applications Claiming Priority (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP00128606A EP1220178A1 (en) | 2000-12-28 | 2000-12-28 | Video smoke detection system |
| EP00128606.1 | 2000-12-28 | ||
| CH1969/2001 | 2001-10-26 | ||
| CH19692001 | 2001-10-26 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1406366A CN1406366A (en) | 2003-03-26 |
| CN1190759C true CN1190759C (en) | 2005-02-23 |
Family
ID=25739035
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB018057217A Expired - Fee Related CN1190759C (en) | 2000-12-28 | 2001-12-20 | Video Smoke detection system |
Country Status (5)
| Country | Link |
|---|---|
| EP (1) | EP1346330B1 (en) |
| CN (1) | CN1190759C (en) |
| AU (1) | AU2002220440B2 (en) |
| HK (1) | HK1054457B (en) |
| WO (1) | WO2002054364A2 (en) |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7280696B2 (en) | 2002-05-20 | 2007-10-09 | Simmonds Precision Products, Inc. | Video detection/verification system |
| US7256818B2 (en) | 2002-05-20 | 2007-08-14 | Simmonds Precision Products, Inc. | Detecting fire using cameras |
| US7245315B2 (en) | 2002-05-20 | 2007-07-17 | Simmonds Precision Products, Inc. | Distinguishing between fire and non-fire conditions using cameras |
| ATE357714T1 (en) * | 2003-07-11 | 2007-04-15 | Siemens Schweiz Ag | METHOD AND DEVICE FOR DETECTING FLAMES |
| EP1519314A1 (en) * | 2003-09-25 | 2005-03-30 | Siemens Building Technologies AG | Method and analysis tool for checking functionality of video surveillance devices and measuring system for carrying out the method |
| AT414055B (en) * | 2003-12-22 | 2006-08-15 | Wagner Sicherheitssysteme Gmbh | PROCESS AND DEVICE FOR FIRE DETECTION |
| GB2430102A (en) * | 2005-09-09 | 2007-03-14 | Snell & Wilcox Ltd | Picture loss detection by comparison of plural correlation measures |
| ES2306020T3 (en) * | 2005-09-09 | 2008-11-01 | Siemens Aktiengesellschaft | SMOKE DETECTION WITH A VIDEO CAMERA. |
| WO2008037293A1 (en) * | 2006-09-25 | 2008-04-03 | Siemens Schweiz Ag | Detection of smoke with a video camera |
| US7859419B2 (en) | 2006-12-12 | 2010-12-28 | Industrial Technology Research Institute | Smoke detecting method and device |
| EP2000952B1 (en) * | 2007-05-31 | 2013-06-12 | Industrial Technology Research Institute | Smoke detecting method and device |
| ATE544140T1 (en) * | 2008-10-14 | 2012-02-15 | Nohmi Bosai Ltd | SMOKE DETECTION DEVICE |
| CN101373553B (en) * | 2008-10-23 | 2010-06-16 | 浙江理工大学 | Early-stage smog video detecting method capable of immunizing false alarm in dynamic scene |
| CN101751744B (en) * | 2008-12-10 | 2011-08-31 | 中国科学院自动化研究所 | Detection and early warning method of smoke |
| CN102023599B (en) * | 2010-02-11 | 2012-08-29 | 北京瑞华赢科技发展有限公司 | Tunnel monitoring system |
| CN102163360B (en) * | 2011-03-24 | 2013-07-31 | 杭州海康威视系统技术有限公司 | Tunnel smoke video detecting method and device |
| DE102016207712A1 (en) * | 2016-05-04 | 2017-11-09 | Robert Bosch Gmbh | Detection device, method for detecting an event and computer program |
| CN106223774B (en) * | 2016-08-27 | 2018-10-02 | 朱洋 | A kind of intelligent window open system distributing object based on image detection smog |
| CN109493361B (en) * | 2018-11-06 | 2021-08-06 | 中南大学 | Fire smoke image segmentation method |
| CN114648852B (en) * | 2022-05-24 | 2022-08-12 | 四川九通智路科技有限公司 | Tunnel fire monitoring method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB9822956D0 (en) * | 1998-10-20 | 1998-12-16 | Vsd Limited | Smoke detection |
-
2001
- 2001-12-20 WO PCT/CH2001/000731 patent/WO2002054364A2/en active Application Filing
- 2001-12-20 AU AU2002220440A patent/AU2002220440B2/en not_active Ceased
- 2001-12-20 EP EP01272590.9A patent/EP1346330B1/en not_active Expired - Lifetime
- 2001-12-20 CN CNB018057217A patent/CN1190759C/en not_active Expired - Fee Related
-
2003
- 2003-09-16 HK HK03106653.2A patent/HK1054457B/en not_active IP Right Cessation
Also Published As
| Publication number | Publication date |
|---|---|
| WO2002054364A3 (en) | 2002-12-19 |
| EP1346330A2 (en) | 2003-09-24 |
| EP1346330B1 (en) | 2013-05-15 |
| HK1054457B (en) | 2005-09-30 |
| WO2002054364A2 (en) | 2002-07-11 |
| HK1054457A1 (en) | 2003-11-28 |
| CN1406366A (en) | 2003-03-26 |
| AU2002220440B2 (en) | 2007-08-23 |
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