WO2002054364A2 - Systeme de detection de fumee base sur la video - Google Patents

Systeme de detection de fumee base sur la video Download PDF

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Publication number
WO2002054364A2
WO2002054364A2 PCT/CH2001/000731 CH0100731W WO02054364A2 WO 2002054364 A2 WO2002054364 A2 WO 2002054364A2 CH 0100731 W CH0100731 W CH 0100731W WO 02054364 A2 WO02054364 A2 WO 02054364A2
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WO
WIPO (PCT)
Prior art keywords
value
smoke detection
detection system
image
smoke
Prior art date
Application number
PCT/CH2001/000731
Other languages
German (de)
English (en)
Other versions
WO2002054364A3 (fr
Inventor
Dieter Wieser
Original Assignee
Siemens Building Technologies Ag
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 EP00128606A external-priority patent/EP1220178A1/fr
Application filed by Siemens Building Technologies Ag filed Critical Siemens Building Technologies Ag
Priority to AU2002220440A priority Critical patent/AU2002220440B2/en
Priority to EP01272590.9A priority patent/EP1346330B1/fr
Publication of WO2002054364A2 publication Critical patent/WO2002054364A2/fr
Publication of WO2002054364A3 publication Critical patent/WO2002054364A3/fr
Priority to HK03106653.2A priority patent/HK1054457B/zh

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Classifications

    • 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 using a video image.
  • smoke detection is carried out with smoke detectors mounted on the ceiling of the respective room, which are based, for example, on the principle of light scattering or light attenuation due to smoke.
  • practically no smoke detectors are used in railway or road tunnels, because the air movement and air stratification caused by the moving cars and trains does not guarantee that the smoke generated by a Brahd would reach the smoke detectors mounted on the ceiling in a useful period.
  • so-called linear heat detection systems such as the FibroLaser system from Siemens Building Technologies AG, Cerberus Division, are used for fire monitoring in tunnels.
  • this method has the problem that smoke is not recognized against a bright background and even fire which produces little smoke is not detected.
  • changes in brightness such as those caused by people moving through the field of view of the camera, can trigger a false alarm.
  • An attempt has been made to solve this problem by examining an outer area in addition to the actual monitored area and interrupting the observation of the monitored area in the event of changes in this outer area.
  • This method has the disadvantage that a fire may not be detected until after a certain delay, and that smoke sources are not recognized in the outer area provided in addition to the surveillance area.
  • the present invention relates to a video smoke detection system with at least one device for recording video images and with a signal processing stage in which the brightness of the individual pixels or groups of pixels of the video images is determined.
  • the object to be achieved with the invention is to provide a video smoke detection system which enables rapid and safe detection of smoke and is particularly suitable for use in road and rail tunnels. Smoke detection should take place at the earliest possible stage of fire and false alarms should be practically excluded.
  • the video smoke detection system is characterized in that the determination of the brightness of the pixels is carried out by a process in which a value representative of the brightness is obtained, and that an examination of the temporal course of the said value for a 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 brightness of the pixels is determined 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 knowledge that the occurrence of smoke leads to the contrast being reduced.
  • the edges are smeared or disappear. This process has the advantage that the edge value is insensitive to global changes in lighting.
  • a second preferred embodiment of the video smoke detection system according to the invention is characterized in that for each pixel the edge value is compared with an average value, and from this comparison a so-called counter image is obtained which shows the temporal behavior of the edge value relative to the average value indicates.
  • the counter image which indicates how often the brightness of the pixel in question has been above the mean value over a certain time, is preferably updated each time the edge value is compared with the mean value.
  • the counter image is compared with a threshold value and if this threshold value is exceeded, an initialization value is added up to form a current 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, the video images are subsequently examined for movements, referred to as motion detection.
  • motion detection In road and rail tunnels, for the monitoring of which the system according to the invention is primarily intended, the covering of edges not caused by smoke will take place almost exclusively by moving objects between the relevant edge and the camera. Since such objects do not suddenly materialize but generally have moved to the place where they cover the edge, it can be assumed that if the edge covering is not caused by smoke, the object covering the edge will move immediately beforehand must have taken place. Motion detection thus provides a reliable criterion for distinguishing edges covered by smoke from those covered by objects.
  • Both the edge values and the movement detection are preferably carried out using counter images which are continuously updated with a hysteresis algorithm.
  • An algorithm based on the normalized cross-correlation is preferably used for the motion detection.
  • the hysteresis algorithm preferably has a minimum and a maximum value and two threshold values lying between them, the counter image jumping to the maximum value when the lower threshold value is exceeded and down to the minimum value when the lower threshold value is being counted down.
  • This hysteresis algorithm enables the use of noisy images for the detection algorithms. An edge caused by noise, with appropriately parameterized hysteresis, will not appear in the counter image, and an edge will not disappear due to 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 eliminating image areas which are not to be taken into account for smoke detection, and the viewed image itself, the edges and the image being preserved between successive iterations of the process and the bit mask being reinitialized 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 pixel by pixel and the analysis of the bit mask is carried out for groups of several pixels referred to below as blocks.
  • a sixth preferred embodiment of the smoke detection system according to the invention is characterized in that the data is processed in two paths, a first path for calculating the edges present in the image and for updating the ones already present data present over edges, and a second path for creating the bit mask, this second path comprising the motion detection.
  • the second path also comprises checking 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.
  • Another preferred embodiment of the smoke detection system according to the invention is characterized in that any image sections can be excluded from the analysis by means of a mask.
  • the bit mask created on the basis of the motion detection and the check for saturation is preferably used to update the counter image for the elimination of image areas which are not to be taken into account for smoke detection.
  • Another preferred embodiment of the smoke detection system according to the invention is characterized in that before the decision about the presence of smoke, a check is carried out to determine whether there is a sufficient number of edges for such a decision.
  • FIG. 1 is a block diagram of a video smoke detection system according to the invention
  • FIG. 5 shows a flowchart to explain the functioning of a second exemplary embodiment of the video smoke detection system according to the invention.
  • the video smoke detection system essentially consists 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 are used for traffic monitoring, for example for monitoring compliance with traffic rules and for detecting congestion, accidents and the like.
  • the cameras are connected to a manned operations center, in which the traffic in the tunnel is monitored via monitors.
  • the processors 2 are arranged in a decentralized manner, a common processor 2 being assigned to a specific number of, for example, 8 to 10 cameras.
  • the video images are broken down into pixels, the individual pixels and / or groups of these are assigned brightness values, and the presence of is decided on the basis of a comparison of the brightness values of the pixels with a reference value Smoke.
  • the brightness values When assigning the brightness values to the individual pixels or pixel groups, it is essential that this assignment is independent of global changes in brightness, that is to say changes in the lighting of the entire image. This independence from the lighting can be achieved by assigning edge values to the pixels, which represent a derivative. The detection of smoke is based on the assumption that the edges are weakened or disappear by smoke.
  • the signal processing and evaluation in the processor 2 can be divided into two function blocks, designated in FIG. 1 with pixel brightness 3 and smoke detection 4.
  • the flow chart of FIG. 2 shows the acquisition of the values representative of the brightness of the pixels (pixel brightness 3) and that of FIG. 3 their further examination for the presence of smoke (smoke detection 4).
  • FIG. 4 shows a flow diagram of additional steps of the method according to FIG. 2 required for certain applications (smoke detection in interior spaces, such as in corridors, foyers and the like).
  • the video images recorded by each camera 1 are broken down into pixels and digitized, as a result of which the intensity value IJJ is determined for each pixel with the coordinates i and j, which can be between 0 and 255, for example.
  • the mean values Mj j or the median, or a value obtained by low-pass filtering, is formed from the intensity values ⁇ for a certain group of pixels of, for example, 3 times 3 or 5 times 5.
  • the median has the advantage that his calculation can be done in 8-bit.
  • an edge value is obtained from the intensity Ij j , which is done by derivation or by frequency analysis (high-pass filtering, for example wavelet transformation).
  • the edge values Kj j of the individual pixels can be determined, for example, by using a Roberts or Sobo 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.
  • the edge value K-, j is above the mean or the median. If YES, a number ⁇ ob is added to a value Z itj and the old value Zj j is replaced by the new one, if NO, a number ⁇ u ⁇ is subtracted from a value Zj j and the old value Zjj is replaced by the new one replaced.
  • the value Z i ⁇ j is a number that indicates how often the edge value and thus the brightness of the pixel in question have been above a certain threshold (mean value or median Mj) on average over a certain time. This number Z J J is referred to below as a counter image.
  • the value range of Zj j is, for example, 0 to 255, the initial value of Zjj when the system is initialized is 0.
  • the numbers ⁇ un and ⁇ ob can be the same or different; for example, both can be equal to one.
  • the counter image Zjj has a particular advantage with regard to the effect of movements on the edge values.
  • an object moves through the image, it also moves at least one edge through this, and this has the consequence that the pixel has a higher edge value at the respective location of the edge, as a result of which the counter image Z ⁇ increases by ⁇ .
  • the counter image Zj j is reduced by ⁇ un , so that in total the passage of edges through the video image in the counter image Zj j of the individual pixels has no effect.
  • , j finally obtained thus preferably represents a value representative of the brightness of the pixel in question.
  • three time scales are used: the frequency of the recorded video images, for example 1/25 second, every 10 seconds after 255 Pictures and about every half hour.
  • the counter image j is compared with a threshold S z . If the counter image Zj j is below the threshold S z , nothing happens; if it is above the threshold S z , a summation takes place, that is to say a value ⁇ x is increased by 1 and replaced by this new value.
  • ⁇ x is significantly larger than ⁇ x °, then new edges have appeared, which can be caused by a stationary object being in the image area of the video camera.
  • a stationary object can be, for example, a standing car in a tunnel or an object parked in a tunnel; in both cases the object covers a certain image area, which is referred to as cover in FIG. 3.
  • the initialization value ⁇ x ° is redefined.
  • the quotient ⁇ x / ⁇ x ° is then formed and compared with a smoke threshold value S R. If the quotient mentioned is below the smoke threshold and edges are weakened or disappeared, an alarm is triggered.
  • the subroutine shown in FIG. 4 is used if necessary, which serves to eliminate movements and starts from the edges Kj j (FIG. 2). In principle, one could also start from the intensity Ij j , but this would be associated with the disadvantage of the presence of disturbing DC components.
  • the difference ⁇ Kjj of successive images is formed and compared with a movement threshold value S B. If ⁇ Kjj is below this threshold, there are no movements.
  • S B the pixels that meet this condition are combined into sub-areas from which the movement is hidden. The latter takes place in that the counter image Zj j is not updated and the last counter image before the movement is used for the subareas mentioned.
  • the signal noise is eliminated by a morphological filter (eroding).
  • a morphological filter eroding. This means the following:
  • the difference image which provides the number of changed pixels in the sub-areas, is a binary image. You move a pattern over this binary image and give the pixels that coincide with the pattern the value "1". The end of the movement is indicated by the subareas successively disappearing from the image and the edges being removed.
  • FIG. 5 shows a flow diagram of a second exemplary embodiment of the video smoke detection system according to the invention, which is characterized in particular by a high level of robustness against interference and a high level of reliability of the smoke detection.
  • the image under consideration is designated by the reference symbol A in FIG. 5.
  • a counter image is a series of values, usually the size of an image, which can be enlarged or reduced. These values are usually used to count events, for example.
  • Both the edge detection and the movement 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, bottom, bottom, high and top, bottom and top forming counter limits that cannot be exceeded or undershot. The value low lies above the value at the bottom and the value high lies between the values low and at the top. If the counter reading is between bottom and bottom or between high and top, counting takes place as normal, i.e. the counter reading is increased by one for each detected event: if the counter reading reaches the low value and another event is detected, it jumps to the top. Similarly, the counter reading jumps to the bottom when it reaches the value high with decreasing values from above.
  • This hysteresis mechanism enables the use of noisy images for the detection algorithms. An edge caused by noise will not appear in the counter image if the hysteresis is appropriately parameterized, and an edge will not disappear due to a single noisy image.
  • the following relationships also apply: The difference between the values low and low results in the number of successive individual images over which a feature or event, for example an edge, must be present in order to be detected, and the difference between the values top and high results the number of consecutive frames after which the event disappears as the counter value decreases. Since this number of individual images corresponds to a certain period of time, these periods of time represent a measure of the response time of the algorithm.
  • the analysis shown in the flowchart begins with an edge detection 5 using a method based, for example, on a Sobel operator.
  • the algorithm analyzes the brightness of each pixel of each frame and tracks the history of the scene with the help of a the mentioned hysteresis mechanism updated counter image 6. Two values are calculated for the surroundings of each pixel:
  • a Sobel edge detection filter is applied to the environment, which delivers a value q Sobe ⁇ ;
  • the counter 6 is increased, if not, it is decreased.
  • the hysteresis mechanism is used in both cases.
  • a movement detection 7 takes place parallel to the edge detection 5, for which an algorithm based on the normalized cross-correlation is used, for example, which roughly proceeds as follows:
  • Small areas of the image for example 4 by 4 pixels, are taken at time t and these pixels are viewed as vector x.
  • the numerator and denominator are multiplied by factors in Formula 1 and the products formed in this way are written in analogy 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 light or lighting conditions affect both sides of the inequality approximately equally, so that the movement detection described is immune to uniform changes in the image. In this way you get 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 when analyzing the counter image 6 of the edges. All blocks of a certain number, for example four by four, pixels in the image are to be detected in which events have occurred which have a negative influence on the smoke detection algorithm. These blocks result in a bit mask 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.
  • the next stage is the correction of the saturation of the video sensor.
  • Such saturation can cause several problems:
  • 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 (elimination of image areas not to be taken into account for smoke detection), again using the hysteresis mechanism already described is applied.
  • each block in the counter image 8 is compared with a threshold value. If the value of the block is above this threshold, all pixels in counter image 6 are set to a minimum value. From the counter image 6 of the edges two sizes 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 value, this second threshold value can roughly be interpreted as the number of pixels in edges present at a previous point in time.
  • a function ic) (c i > l) is defined, which
  • the two sizes can now be calculated by estimating the number of pixels currently lying on an edge by / very close to the maximum value W m that the pixels c 1, can reach.
  • the value (W m - k) is generally selected for /, where k means a number of frames and is, for example, about 250 in the case of a conventional fixed camera in a tunnel.
  • a parameter "image height” can be added to the subroutine for counting the pixels, which means that only the upper part, for example the upper half, of the image is taken into account for smoke detection. This makes sense because smoke generally rises.
  • any image sections can be excluded from the analysis with a mask.
  • a step 12 is now used to check whether there are enough edges to be able 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. In this case, since it is impossible to detect a fire, a fault signal should be triggered, which indicates that the algorithm cannot work under the current circumstances. In order to briefly delay further actions and to be less sensitive to noise, an interrupt value is used, which can either be zero or greater than zero. In the latter case, it had been detected shortly before that there were not enough edges.
  • the difference is multiplied by a parameter and compared with the sum. If the sum is greater, there is no smoke; otherwise the alarm is triggered. In both cases, processing of the current image is finished and processing of the next one begins.
  • the alarm can be triggered, for example, by a corresponding alarm being displayed in a manned alarm or monitoring center to which the camera in question is connected, which prompts the operating personnel to analyze the image supplied by the camera in question by eye.
  • the said center can be, for example, a police or fire service center in an urban or regional base or the command center of a road tunnel.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • Business, Economics & Management (AREA)
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Abstract

Système de détection de fumée basé sur la vidéo, qui comporte au moins un dispositif de prise d'images vidéo (A) et une étape de traitement de signaux lors de laquelle la luminosité des différents pixels ou de groupes de pixels des images vidéo (A) est déterminée. La détermination de la luminosité est opérée à l'aide d'un procédé selon lequel une valeur représentative de la luminosité est obtenue. L'évolution temporelle de la valeur susmentionnée est examinée à la recherche d'une modification caractéristique de l'apparition de fumée. La détermination de la luminosité des pixels est effectuée à l'aide d'un processus (5) d'extraction de contours lors duquel une valeur de contour est attribuée à chaque pixel. Le processus (5) d'extraction de contours est suivi d'un examen des images vidéo (A) à la recherche de mouvements, désigné par le terme de détection de mouvement (7). Tant la détermination des valeurs de contour que la détection de mouvement (7) sont effectuées à l'aide d'images de comptage (6, 8) qui sont actualisées en permanence à l'aide d'un algorithme d'hystérésis.
PCT/CH2001/000731 2000-12-28 2001-12-20 Systeme de detection de fumee base sur la video WO2002054364A2 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
AU2002220440A AU2002220440B2 (en) 2000-12-28 2001-12-20 Video smoke detection system
EP01272590.9A EP1346330B1 (fr) 2000-12-28 2001-12-20 Systeme de detection de fumee base sur la video
HK03106653.2A HK1054457B (zh) 2000-12-28 2003-09-16 視頻烟霧檢測系統

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
EP00128606A EP1220178A1 (fr) 2000-12-28 2000-12-28 Système video de détection de fumée
EP00128606.1 2000-12-28
CH1969/01 2001-10-26
CH19692001 2001-10-26

Publications (2)

Publication Number Publication Date
WO2002054364A2 true WO2002054364A2 (fr) 2002-07-11
WO2002054364A3 WO2002054364A3 (fr) 2002-12-19

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

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EP1496483A1 (fr) * 2003-07-11 2005-01-12 Siemens Building Technologies AG Mèthode et dispositif de détection de flammes
EP1519314A1 (fr) * 2003-09-25 2005-03-30 Siemens Building Technologies AG Procédé et outil d'analyse pour tester la fonctionalité d'un dispositif de surveillance vidéo et système de mésure pour la mise en oeuvre du procédé
AT414055B (de) * 2003-12-22 2006-08-15 Wagner Sicherheitssysteme Gmbh Verfahren und einrichtung zur branderkennung
EP1762995A1 (fr) * 2005-09-09 2007-03-14 Siemens Schweiz AG Détection de fumée avec une caméra vidéo
EP1762978A2 (fr) * 2005-09-09 2007-03-14 SNELL & WILCOX LIMITED Analyse d'image pour la détection de la perte d'une image dans une série d'images
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
WO2008037293A1 (fr) * 2006-09-25 2008-04-03 Siemens Schweiz Ag Détection de fumée avec une caméra vidéo
EP2000952A3 (fr) * 2007-05-31 2010-06-16 Industrial Technology Research Institute Procédé et dispositif de détection de fumée
US7859419B2 (en) 2006-12-12 2010-12-28 Industrial Technology Research Institute Smoke detecting method and device
CN102023599A (zh) * 2010-02-11 2011-04-20 北京瑞华赢科技发展有限公司 一种隧道监控系统
WO2017190882A1 (fr) * 2016-05-04 2017-11-09 Robert Bosch Gmbh Dispositif de détection, procédé de détection d'un événement et programme informatique

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EP2461300B1 (fr) * 2008-10-14 2014-11-26 Nohmi Bosai Ltd. Appareil de détection de fumée
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
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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
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
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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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Cited By (17)

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Publication number Priority date Publication date Assignee Title
US7280696B2 (en) 2002-05-20 2007-10-09 Simmonds Precision Products, Inc. Video detection/verification system
US7302101B2 (en) 2002-05-20 2007-11-27 Simmonds Precision Products, Inc. Viewing a compartment
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
EP1496483A1 (fr) * 2003-07-11 2005-01-12 Siemens Building Technologies AG Mèthode et dispositif de détection de flammes
EP1519314A1 (fr) * 2003-09-25 2005-03-30 Siemens Building Technologies AG Procédé et outil d'analyse pour tester la fonctionalité d'un dispositif de surveillance vidéo et système de mésure pour la mise en oeuvre du procédé
AT414055B (de) * 2003-12-22 2006-08-15 Wagner Sicherheitssysteme Gmbh Verfahren und einrichtung zur branderkennung
EP1762978A2 (fr) * 2005-09-09 2007-03-14 SNELL & WILCOX LIMITED Analyse d'image pour la détection de la perte d'une image dans une série d'images
EP1762995A1 (fr) * 2005-09-09 2007-03-14 Siemens Schweiz AG Détection de fumée avec une caméra vidéo
EP1762978A3 (fr) * 2005-09-09 2012-05-09 Snell Limited Analyse d'image pour la détection de la perte d'une image dans une série d'images
US8238691B2 (en) 2005-09-09 2012-08-07 Snell & Wilcox Limited Method of and apparatus for image analysis
WO2008037293A1 (fr) * 2006-09-25 2008-04-03 Siemens Schweiz Ag Détection de fumée avec une caméra vidéo
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EP2000952A3 (fr) * 2007-05-31 2010-06-16 Industrial Technology Research Institute Procédé et dispositif de détection de fumée
CN102023599A (zh) * 2010-02-11 2011-04-20 北京瑞华赢科技发展有限公司 一种隧道监控系统
WO2017190882A1 (fr) * 2016-05-04 2017-11-09 Robert Bosch Gmbh Dispositif de détection, procédé de détection d'un événement et programme informatique
US10560667B2 (en) 2016-05-04 2020-02-11 Robert Bosch Gmbh Detection device, method for detection of an event, and computer program

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EP1346330A2 (fr) 2003-09-24
CN1406366A (zh) 2003-03-26
AU2002220440B2 (en) 2007-08-23
EP1346330B1 (fr) 2013-05-15
CN1190759C (zh) 2005-02-23
WO2002054364A3 (fr) 2002-12-19
HK1054457A1 (en) 2003-11-28
HK1054457B (zh) 2005-09-30

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