EP1346330B1 - Video-rauchdetektionssystem - Google Patents

Video-rauchdetektionssystem Download PDF

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Publication number
EP1346330B1
EP1346330B1 EP01272590.9A EP01272590A EP1346330B1 EP 1346330 B1 EP1346330 B1 EP 1346330B1 EP 01272590 A EP01272590 A EP 01272590A EP 1346330 B1 EP1346330 B1 EP 1346330B1
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EP
European Patent Office
Prior art keywords
value
smoke detection
image
detection system
smoke
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.)
Expired - Lifetime
Application number
EP01272590.9A
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German (de)
English (en)
French (fr)
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EP1346330A2 (de
Inventor
Dieter Wieser
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.)
Siemens AG
Original Assignee
Siemens AG
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Publication date
Priority claimed from EP00128606A external-priority patent/EP1220178A1/de
Application filed by Siemens AG filed Critical Siemens AG
Priority to EP01272590.9A priority Critical patent/EP1346330B1/de
Publication of EP1346330A2 publication Critical patent/EP1346330A2/de
Application granted granted Critical
Publication of EP1346330B1 publication Critical patent/EP1346330B1/de
Anticipated expiration legal-status Critical
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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
    • 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 is in the field of smoke detection based on a video image.
  • the smoke detection is carried out with mounted on the ceiling of each room smoke detectors, which are based for example on the principle of light scattering or light attenuation by smoke.
  • railway or road tunnels on the other hand, virtually no smoke detectors are used because, because of the movement of air and stratification caused by the moving cars and trains, it is not guaranteed that the smoke produced in a fire would reach the ceiling mounted smoke detectors within a reasonable time.
  • so-called linear heat reporting systems such as the FibroLaser system from Siemens Building Technologies AG, Cerberus Division, are used today for tunnel monitoring in tunnels.
  • the WO 00/23959 A discloses a video smoke detection system including a video camera, video image comparison means, signal processing means and alerting means which is responsive to the output of the signal processing means.
  • the signal processing means successively analyzes the images from the video camera and compares the intensity and / or color of the individual pixels or groups of pixels to decide whether there is a change characteristic of smoke.
  • the present invention relates to a video smoke detection system comprising at least one means for capturing video images and having a signal processing stage in which a determination is made of the brightness of the individual pixels or groups of pixels of the video images.
  • the problem to be solved by the invention is to specify a video smoke detection system which enables rapid and reliable detection of smoke and is particularly suitable for use in road and rail tunnels.
  • the smoke detection should take place in the earliest possible stage of the fire and false alarms should be virtually eliminated.
  • the video smoke detection system is characterized in that the determination of the brightness of the pixels takes place by a process in which a value representative of the brightness is obtained, and in that an examination of the time characteristic of said value is characteristic of the occurrence of smoke Change takes place.
  • a first preferred embodiment of the video smoke detection system according to the invention is characterized in that the determination of the brightness of the pixels takes place by an edge extraction process in which an edge value is assigned to each pixel.
  • the smoke detection system according to the invention is based on the recognition that the occurrence of smoke leads to the fact that the contrast is reduced.
  • the edges are smeared or disappear. This process has the advantage that the edge value is insensitive to global lighting changes.
  • a second preferred embodiment of the video smoke detection system according to the invention is characterized in that for each pixel a comparison of the edge value with an average value is made, and that a subsequently so-called counter image is obtained from this comparison, which indicates the temporal behavior of the edge value relative to the mean value.
  • the counter image which indicates how often the brightness of the relevant pixel has averaged above said mean value over a certain time, is updated with each comparison of the edge value with the mean value.
  • a third preferred embodiment of the video smoke detection system according to the invention is characterized in that, in addition to the edge extraction process, an examination of the video images, referred to below as motion detection, takes place on movements.
  • Both the determination of the edge values and the motion detection preferably take place on the basis of counter images, which are continuously updated with a hysteresis algorithm.
  • an algorithm based on the normalized cross-correlation is preferably used.
  • the hysteresis algorithm preferably has a minimum and a maximum value and two threshold values lying between them, wherein the counter image jumps to the maximum value when counting up when the lower threshold value is exceeded and to the minimum value when counting down when the upper threshold value is undershot.
  • This hysteresis algorithm enables the use of noisy images for the detection algorithms. An edge caused by noise will not appear in the counter image, with appropriately parameterized hysteresis, and an edge will not disappear because of a single noisy image.
  • a fourth preferred embodiment of the smoke detection system according to the invention is characterized in that three data structures are used, a data field with information about the edges present in the respective image, a data field with a bit mask for the purpose of excreting image areas which are not to be considered for smoke detection, and the viewed image itself, with the edges and image preserved between successive iterations of the process and the bitmask re-initialized for each iteration.
  • a fifth preferred embodiment of the smoke detection system according to the invention is characterized in that the image and the edges are analyzed on a pixel-by-pixel basis and the analysis of the bit mask is carried out for groups of several pixels hereinafter referred to as blocks.
  • a sixth preferred embodiment of the smoke detection system according to the invention is characterized in that the processing of the data takes place on two paths, a first path for calculating the edges present in the image and for updating the already data present over edges, and a second path to create the bitmask, this second path comprising motion detection.
  • the second path also comprises a check of the blocks for saturation of the device for recording the video images, in which blocks are marked with a certain number of saturated pixels and are not taken into account for the analysis of the counter image of the edges.
  • a further preferred embodiment of the smoke detection system according to the invention is characterized in that any image detail can be excluded from the analysis by means of a mask.
  • the bitmask created by motion detection and saturation checking is used to update the counter image for the elimination of image areas not to be considered for smoke detection.
  • a further preferred embodiment of the smoke detection system according to the invention is characterized in that prior to the decision on the presence of smoke, a check is made as to whether there is a sufficient number of edges for such a decision.
  • the inventive video smoke detection system is according to Fig. 1 essentially of a number of video cameras 1 and a common processor 2, in which the processing and evaluation of the signals of the video cameras 1 takes place.
  • the video cameras 1 are mounted, for example, in a road tunnel and serve for traffic monitoring, for example, to monitor compliance with traffic regulations and for the detection of congestion, accidents and the like.
  • the cameras are connected to a manned operations center, in which the traffic in the tunnel is monitored by monitors.
  • the processors 2 are arranged in a decentralized manner, wherein a common number of, for example, 8 to 10 cameras is assigned to a common processor 2 in each case.
  • the video images are decomposed into pixels, the individual pixels and / or groups thereof are assigned brightness values, and based on a comparison of the brightness values of the pixels with a reference value, the decision is made on the presence of Smoke.
  • assigning the brightness values to the individual pixels or pixel groups it is essential that this assignment of global brightness changes, ie changes in the illumination of the entire image, is independent. This independence from the lighting can be achieved by assigning edge values to the pixels, which are indeed a derivative.
  • the detection of smoke is based on the assumption that the edges are attenuated by smoke or disappear.
  • the signal processing and evaluation in the processor 2 can be divided into two in Fig. 1 be divided with pixel brightness 3 and smoke detection 4 designated function blocks. According to this division, the flowchart of FIG Fig. 2 obtaining the values representative of the brightness of the pixels (pixel brightness 3) and that of Fig. 3 their further investigation on the presence of smoke (smoke detection 4).
  • Fig. 4 shows a flow chart of required for certain applications (smoke detection indoors, such as in aisles, foyers and the like) additional steps of the method according to Fig. 2 ,
  • the video images taken by each camera 1 are decomposed into pixels and digitized, whereby for each pixel with the coordinates i and j its intensity value I i, j is determined, which may for example be between 0 and 255. From the intensity values I i, j , the mean value M i, j or the median is formed for a specific group of pixels of, for example, 3 times 3 or 5 times 5, or a value obtained by a low-pass filtering.
  • the median has the advantage that its calculation can be done in 8-bit.
  • an edge value is obtained from the intensity I i, j , which is done by a derivative or by a frequency analysis (high-pass filtering, for example wavelet transformation).
  • the edge values K i, j of the individual pixels can be determined, for example, by using a Roberts or a Sobel operator. Of course you can also use a more complicated operator for the edge calculation and apply it to larger areas such as 5x5 or 7x7 pixels.
  • a value Z i, j is a number ⁇ whether counted to it and the previous value Z i, j is replaced by the new one if NO is withdrawn un from a value Z i, j ⁇ is a number, and the old Value Z i, j is replaced by the new one.
  • the value Z i, j is a number which indicates how often the edge value and thus the brightness of the respective pixel has averaged above a certain threshold (mean or median M i, j ) for a certain time. This number Z i, j is referred to below as a counter image.
  • the value range of Z i, j is eg 0 to 255, the initial value of Z i, j at the initialization of the system is 0.
  • the numbers ⁇ un and ⁇ ob may be the same or different; for example, both can be equal to one.
  • the counter image Z i, j has a particular advantage in terms of the effect of movements on the edge values.
  • an object moves through the image, it also moves at least one edge therethrough, and this results in the pixel having a higher edge value at the respective location of the edge, whereby the counter image Z i, j increases by ⁇ .
  • the counter image Z i, j is reduced by ⁇ un , so that in total the passage of edges through the video image in the counter image Z i, j of the individual pixels does not have any effect.
  • the finally obtained counter image Z i, j thus preferably represents a value representative of the brightness of the relevant pixel.
  • three time scales are used: the frequency of the recorded video images, for example 1/25 second, every 10th Seconds after 255 pictures and about every half an hour.
  • the counter image Z i, j is compared with a threshold S z . If the counter image Z i, j is below the threshold S z , nothing happens if it is above the threshold S z , then there is a summation, that is, a value ⁇ x is increased by 1 and replaced by this new value.
  • ⁇ x is significantly larger than ⁇ x 0 , then new edges have occurred, which can be caused by the presence of a stationary object in the image area of the video camera.
  • a stationary object in the image area of the video camera.
  • Such an object may be in a tunnel, for example, a stationary car or a gear in this parked object; in both cases, the object covers a certain image area, which is in Fig. 3 With cover is designated.
  • the initialization value ⁇ x 0 is redefined.
  • the quotient ⁇ x / ⁇ x 0 is formed and compared with a smoke threshold S R. If the mentioned quotient is below the smoke threshold and thus edges are weakened or disappeared, an alarm is triggered.
  • the in Fig. 4 used subroutine, which serves for the elimination of movements and from the edges K i, j ( Fig. 2 ).
  • the intensity I i, j but this would be associated with the disadvantage of the presence of interfering DC components.
  • S B a motion threshold value
  • ⁇ K i, j is below this threshold, there are no movements.
  • ⁇ K i, j > S B the pixels which fulfill this condition are combined to subareas from which the motion is masked out. The latter takes place in that the counter image Z i, j is not updated and the last counter image before the movement is used for the abovementioned subareas.
  • a morphological filter This means the following:
  • the difference image that provides the number of changed pixels in the subareas is a binary image. You will go over this binary image with a pattern and give the pixels that match the pattern the value "1". The end of the movement is indicated by the fact that the sub-areas one after the other disappear from the picture and the edges decrease.
  • Fig. 5 shows a flowchart of a second embodiment of the inventive video smoke detection system, which is characterized in particular by a high robustness to interference and high reliability of smoke detection.
  • the viewed image is in Fig. 5 denoted by the reference A.
  • Another point to be considered in smoke detection is that of the different time grids which, on the one hand, are to be considered and on the other hand to be distinguished from one another.
  • There are very fast effects in the subsecond range such as camera shake caused by a nearby truck, which can be eliminated by forming a moving average.
  • One way to distinguish these time slots and to identify effects in the right time frame are counter images with hysteresis.
  • a counter image is a series of values, usually the size of an image, which can be enlarged or reduced. These values are commonly used to count events, for example. Both edge detection and motion detection of the algorithm shown in the flowchart depend on counter images which are updated with a hysteresis algorithm.
  • the hysteresis is characterized by four values, at the bottom, deep, high and uppermost, where at the bottom and at the top are counter limits that can not be fallen below or exceeded. The value deep lies above the value at the bottom and the value high lies between the values deep and at the top. If the count is between low and low, or between high and high, counting is normal, i. the counter reading is increased by one per detected event: however, if the counter reading reaches the value low and detects another event, it jumps to the top. Similarly, the counter reading jumps to the bottom when decreasing values from the top.
  • This hysteresis mechanism allows the use of noisy images for the detection algorithms. An edge caused by noise will not appear in the counter image, with appropriately parameterized hysteresis, and an edge will not disappear because of a single noisy image.
  • the following relationships apply: The difference between the values low and low results in the number of consecutive frames over which a feature or event, such as an edge, must be present to be detected, and gives the difference between the upper and lower values the number of consecutive frames after which the event disappears as the counter value decreases. Since this number of individual images each corresponds to a specific period of time, these time periods represent a measure of the reaction time of the algorithm.
  • a motion detection 7 takes place, for which, for example, an algorithm based on the normalized cross-correlation is used, which roughly runs as follows:
  • the normalized cross-correlation is: x ⁇ ⁇ y ⁇ ⁇ x ⁇ ⁇ ⁇ ⁇ y ⁇ ⁇
  • numerator and denominator are multiplied by factors in formula 1, and the products formed thereby are written to analogously to the standardized cross-correlation according to formula 1. If the counter is smaller, then a movement has taken place and the corresponding area is marked. Sudden changes in the lighting or lighting conditions affect both sides of the inequality approximately equally, so that the described motion detection is immune to uniform changes in the image. This gives a map of the current image with 4 by 4 pixel blocks.
  • the next step is the calculation of the blocks that should not be taken into account in the analysis of the counter image 6 of the edges. It should all blocks of a certain number, for example, four times four, pixels are detected in the image in which events have taken place, which adversely affect the smoke detection algorithm. These blocks result in a bitmask 8 which is represented as a counter image of 1/16 the size of the complete image becomes. The size of the blocks is determined by the blocks considered in the motion detection, but can be changed.
  • bit mask 8 calculated in the stages of motion detection 7, saturation check 9 and expansion operator 10 is now used to update the counter image for the elimination process 11 (excretion of image areas not to be considered for smoke detection), again using the hysteresis mechanism already described ,
  • each block in counter image 8 is compared to a threshold. If the value of the block is above this threshold, all pixels in the counter image 6 are set to a minimum value. From the counter image 6 of the edges, two quantities are now calculated, which represent the number of edges at different times.
  • the first size is the number of pixels in the currently existing edges above a first threshold.
  • the second size is the number of pixels above a second threshold, and this second threshold may be interpreted roughly as the number of pixels in edges present at a time.
  • ⁇ l c ⁇ i . j c i . j > l which counts the number of pixels c i, j in the image of the counters having a value above a threshold value l .
  • the two magnitudes can now be calculated by estimating the number of pixels currently at an edge by placing 1 very close to the maximum value W m that the pixels c ij can reach.
  • the value ( W m -k ) is usually chosen for 1 , where k means a number of frames and, for example, is about 250 for a conventional fixed camera in a tunnel.
  • the subroutine for counting the pixels may be accompanied by an "image height" parameter which causes only the upper part, for example the upper half, of the image to be considered for smoke detection. This makes sense because smoke usually rises.
  • any image section can be excluded from the analysis with a mask.
  • a step 12 Before deciding whether smoke is present, it is now checked in a step 12 whether there are enough edges to make this decision. This check is necessary in the case where, for example, a large truck is directly in front of the camera and the image has no edges. Since it is impossible to detect a fire in this case, a fault signal should be triggered indicating that the algorithm can not operate under the current circumstances. To delay further actions and to be less sensitive to noise, an interrupt value is used, which may be either zero or greater than zero. In the latter case, it had been detected shortly before that there were not enough edges.
  • break value is already nonzero, it is reduced, and if it reaches one, a fault signal is triggered. On the other hand, if the break value is zero, it is increased to a value greater than zero. If there are enough edges, the break value is reset and processing continues.
  • a step 13 the decision is made on the presence of smoke based on the average sum and difference of the edges. The difference is multiplied by a parameter and compared to the sum. If the sum is greater, there is no smoke; otherwise an alarm will be triggered. In both cases, the processing of the current image is finished and processing of the next one begins.
  • the alarm can be triggered, for example, by displaying a corresponding alarm in a manned alarm or monitoring center to which the relevant camera is connected, which causes the operator to analyze the image of the eye supplied by the relevant camera in more detail.
  • the said center may be, for example, a police or fire station in an urban or regional base or the command center of a road tunnel.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Fire-Detection Mechanisms (AREA)
  • Image Analysis (AREA)
EP01272590.9A 2000-12-28 2001-12-20 Video-rauchdetektionssystem Expired - Lifetime EP1346330B1 (de)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP01272590.9A EP1346330B1 (de) 2000-12-28 2001-12-20 Video-rauchdetektionssystem

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
EP00128606A EP1220178A1 (de) 2000-12-28 2000-12-28 Video-Rauchdetektionssystem
EP00128606 2000-12-28
CH19692001 2001-10-26
CH196901 2001-10-26
EP01272590.9A EP1346330B1 (de) 2000-12-28 2001-12-20 Video-rauchdetektionssystem
PCT/CH2001/000731 WO2002054364A2 (de) 2000-12-28 2001-12-20 Video-rauchdetektionssystem

Publications (2)

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EP1346330A2 EP1346330A2 (de) 2003-09-24
EP1346330B1 true EP1346330B1 (de) 2013-05-15

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EP (1) EP1346330B1 (zh)
CN (1) CN1190759C (zh)
AU (1) AU2002220440B2 (zh)
HK (1) HK1054457B (zh)
WO (1) WO2002054364A2 (zh)

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US7245315B2 (en) 2002-05-20 2007-07-17 Simmonds Precision Products, Inc. Distinguishing between fire and non-fire conditions using cameras
US7256818B2 (en) 2002-05-20 2007-08-14 Simmonds Precision Products, Inc. Detecting fire using cameras
US7280696B2 (en) 2002-05-20 2007-10-09 Simmonds Precision Products, Inc. Video detection/verification system
DE50306852D1 (de) * 2003-07-11 2007-05-03 Siemens Schweiz Ag Verfahren und Einrichtung zur Detektion von Flammen
EP1519314A1 (de) * 2003-09-25 2005-03-30 Siemens Building Technologies AG Verfahren und Analysewerkzeug für die Überprüfung der Funktionstauglichkeit von Video-Überwachungsanlagen, sowie Messeinrichtung zur Durchführung des Verfahrens
AT414055B (de) * 2003-12-22 2006-08-15 Wagner Sicherheitssysteme Gmbh Verfahren und einrichtung zur branderkennung
DE502005004026D1 (de) * 2005-09-09 2008-06-19 Siemens Ag Detektion von Rauch mit einer Videokamera
GB2430102A (en) * 2005-09-09 2007-03-14 Snell & Wilcox Ltd Picture loss detection by comparison of plural correlation measures
CN101395643B (zh) * 2006-09-25 2011-12-14 西门子公司 利用摄像机检测烟雾
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
EP2461300B1 (en) * 2008-10-14 2014-11-26 Nohmi Bosai Ltd. Smoke detecting apparatus
CN101373553B (zh) * 2008-10-23 2010-06-16 浙江理工大学 一种在动态场景中能免疫误报的早期烟雾视频检测方法
CN101751744B (zh) * 2008-12-10 2011-08-31 中国科学院自动化研究所 一种烟雾检测和预警方法
CN102023599B (zh) * 2010-02-11 2012-08-29 北京瑞华赢科技发展有限公司 一种隧道监控系统
CN102163360B (zh) * 2011-03-24 2013-07-31 杭州海康威视系统技术有限公司 隧道烟雾的视频检测方法及其装置
DE102016207712A1 (de) * 2016-05-04 2017-11-09 Robert Bosch Gmbh Detektionsvorrichtung, Verfahren zur Detektion eines Ereignisses und Computerprogramm
CN106223774B (zh) * 2016-08-27 2018-10-02 朱洋 一种基于影像探测烟雾散发物体的智能窗户开启系统
CN109493361B (zh) * 2018-11-06 2021-08-06 中南大学 一种火灾烟雾图像分割方法
CN114648852B (zh) * 2022-05-24 2022-08-12 四川九通智路科技有限公司 一种隧道火灾监测方法及系统

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HK1054457B (zh) 2005-09-30
HK1054457A1 (en) 2003-11-28
EP1346330A2 (de) 2003-09-24
WO2002054364A2 (de) 2002-07-11
WO2002054364A3 (de) 2002-12-19
CN1406366A (zh) 2003-03-26
CN1190759C (zh) 2005-02-23
AU2002220440B2 (en) 2007-08-23

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