JP2010097412A - Smoke detecting apparatus - Google Patents

Abstract

A smoke detection device capable of detecting smoke with high sensitivity while suppressing the influence of disturbance.
A smoke detection apparatus that detects smoke generation by performing image processing on an image captured by a surveillance camera, and stores a plurality of images captured in time series by the surveillance camera. Based on the memory 10 and a plurality of images stored in the image memory 10, a luminance histogram of the same pixel is calculated for each predetermined pixel a plurality of times within a certain past period, and an intruder or smoke is calculated based on the luminance histogram. A smoke detection area selecting unit 20 that detects presence / absence of a luminance value newly generated by generation and specifies a candidate area to be subjected to image processing is provided.
[Selection] Figure 1

Description

  The present invention relates to a smoke detection device that detects the generation of smoke by performing image processing on an image captured by a surveillance camera.

  From the viewpoint of initial fire extinguishment in the event of a fire or prevention of escape delay in a fire accident, early detection of fire or smoke is very important. Thus, in the field of smoke detection devices, research has been conducted on early detection of smoke by performing image processing on an image captured by a surveillance camera.

  As an example, there is a conventional smoke detection device that detects smoke by installing a camera in a tunnel or the like and performing image processing on an image captured by the camera. In image processing for detecting smoke, generally, a reference image (reference image) is stored in advance, a difference image between the latest captured image and the reference image is calculated, and a region where a change has occurred is calculated. Extraction detects smoke. (For example, refer to Patent Document 1).

  In addition, the reference image is regularly updated in order to cope with the temporal change of the reference image due to the influence of sunlight.

Thus, by performing image processing on the image captured by the camera and performing smoke detection, the following two merits can be obtained.
1) By visually confirming the image of the surveillance camera, it is possible to grasp the smoke detection status in a remote place.
2) It is possible to divert already installed surveillance cameras and construct efficient equipment.

Japanese Patent No. 3909665

However, the prior art has the following problems.
In the prior art, in order to detect smoke, a pixel region where a luminance difference from a frame difference image or a background image exceeds a predetermined threshold is extracted. However, for example, when a captured image changes due to an influence of a change in surrounding lighting conditions, there is a problem that a portion where smoke is not generated is erroneously detected as smoke.

  In addition, the smoke itself that is a detection target has a small color tone, and depending on the background color, there may be a small change in luminance (luminance difference) in the captured image. It may be difficult to set and high-sensitivity smoke detection may not be possible.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to obtain a smoke detection device capable of performing smoke detection with high sensitivity while suppressing the influence of disturbance.

  A smoke detection device according to the present invention is a smoke detection device that detects the generation of smoke by performing image processing on an image captured by a monitoring camera, and a plurality of images captured in time series by the monitoring camera Based on the image memory for storing the image and the plurality of images stored in the image memory, the luminance histogram of the same pixel is calculated for each predetermined pixel over a plurality of times within a certain past period, and the intrusion is performed based on the luminance histogram. A smoke detection area selection unit that detects presence / absence of a brightness value newly generated by generation of an object or smoke, and identifies a candidate area to be subjected to image processing.

  According to the smoke detection device of the present invention, a luminance histogram is calculated within a predetermined past period for each predetermined pixel from a plurality of images captured in time series, and newly generated based on the calculated luminance histogram. By detecting the presence or absence of the brightness value and specifying the smoke detection area, it is possible to obtain a smoke detection device capable of detecting smoke with high sensitivity while suppressing the influence of disturbance.

  Hereinafter, a preferred embodiment of a smoke detection device of the present invention will be described with reference to the drawings.

Embodiment 1 FIG.
FIG. 1 is a configuration diagram of a smoke detection device according to Embodiment 1 of the present invention. The smoke detection apparatus according to the first embodiment includes an image memory 10, a smoke detection area selection unit 20, and a smoke generation detection unit 30. The image memory 10 is configured as an image memory for a plurality of frames so that images captured by the camera 1 can be stored as time-series data for a certain period in the past.

  The smoke detection area selection unit 20 includes a luminance histogram creation unit 21, a luminance change determination unit 22, and a detection candidate area specifying unit 23. The smoke detection area selection unit 20 has a function of specifying an area where smoke detection is to be performed as a smoke detection candidate area based on an image for a predetermined period of time captured by the camera 1 and stored in the image memory 10. Have.

  The smoke generation detection unit 30 includes a smoke feature amount calculation unit 31, a mapping unit 32, and a smoke determination unit 33. Then, the smoke generation detection unit 30 calculates a feature amount for detecting the generation of smoke for the smoke detection candidate region specified by the smoke detection area selection unit 20, and whether smoke is generated based on the calculation result. It has a function to determine whether or not. For example, as the feature quantity of smoke, the brightness decreases due to smoke entering a certain area, the brightness of the entire area converges to a specific brightness value, and the change in average brightness is a rule like an artificial light source. Since there are features such as not exhibiting sex, the feature amount of smoke is calculated based on these features.

  By providing such a configuration, the smoke detection device according to the first embodiment performs efficient image processing by performing image processing for detecting the presence or absence of smoke on the specified smoke detection candidate region. Sensitive smoke detection can be realized.

  The present invention does not perform smoke detection on all of the captured images, but detects smoke only on a smoke detection candidate region that is a divided region obtained by dividing the image into a plurality of blocks. It is characterized by performing. That is, the present invention has a technical feature in that it has a function of the smoke detection area selection unit 20. First, the function of the smoke detection area selection unit 20 will be described.

Step 1: Function of Luminance Histogram Creation Unit 21 The luminance histogram creation unit 21 determines whether or not an intruder or smoke has changed in the captured image for a predetermined period of time for each predetermined pixel. This is done by calculating a histogram of the brightness of the inside.

  Here, the intruder corresponds to a passerby who temporarily passes through the image. The luminance histogram corresponds to a frequency distribution indicating what luminance value distribution the target pixel has in the time-series data of a certain past period.

  FIG. 2 is a diagram showing an example of a luminance histogram created by the luminance histogram creating means 21 in Embodiment 1 of the present invention. Assuming a state where no intruders are generated and no smoke is generated, if the frequency of occurrence of luminance values in a plurality of images within a certain past period is counted for a certain pixel, no change occurs for a certain period. As shown in FIG. 2A, the distribution is concentrated on a narrow range of luminance, and the frequency of occurrence of the narrow range is high. Here, the occurrence frequency is the “existence frequency” on the vertical axis in FIG. 2. In other words, the number of images taken in a certain period is the maximum value, and the position coordinates are the same in these images. This is the number of times that a specific pixel has the same luminance value. This histogram shows the brightness value of each pixel in the past. If there is no intruder, the existing brightness is distributed in a narrow range, and the number of times of existence remains high. Keep doing.

  On the other hand, assuming that intruders are generated or smoke is generated, the frequency of occurrence of luminance values in a plurality of images in a certain past period is totaled for a certain pixel. Since a change occurs in the image, as shown in FIG. 2B, a luminance value different from the distribution in the past starts to appear in places other than the narrow range of luminance in FIG. As a matter of course, the number of existing luminances is small. That is, in addition to the high and narrow luminance distribution, the luminance detected as a different distribution can be obtained from the image after the intruder is generated or after the smoke is generated.

Then, the frequency of occurrence of the luminance indicating the newly appearing different distribution is shown in FIG. 2A when the number of images after the occurrence of intruders or smoke is small among the plurality of images in the past fixed period. The result is less than the frequency of occurrence of luminance in a narrow range before occurrence.
In addition, even when the difference between the luminance distribution before the occurrence and the luminance distribution after the occurrence is slight, the occurrence of intruders or smoke can be obtained by obtaining histograms for each pixel based on a plurality of images within a certain past period. Can be identified with high accuracy.

  Further, the luminance histogram creating means 21 counts the obtained luminance with a certain width when creating the luminance histogram (for example, if the luminance is 100, three luminances including 99 and 101 which are the previous and subsequent luminances) are counted. Luminance (counting as luminance values 99, 100, and 101) requires less computation for calculating the histogram, and can obtain a result of reducing the influence of noise. Moreover, the result of reducing the influence of noise can also be obtained by performing a smoothing process after obtaining the luminance histogram. In creating the luminance histogram, a histogram may be created for each pixel, but the histogram may be shared by adjacent pixels. For example, a luminance histogram may be created by grouping a total of four pixels in two vertical and horizontal directions, or by grouping a total of nine pixels in three vertical and horizontal directions. Time can be shortened. Preferably, by combining four pixels together, in this case, the processing time is short and the accuracy of intrusion detection is not reduced.

Step 2: Function of the luminance change determination means 22 The luminance change determination means 22 is newly generated by the occurrence of an intruder or smoke based on the luminance histogram calculated (generated) individually for each predetermined pixel by the luminance histogram creation means 21. The presence / absence of a luminance value generated in the above is detected. For example, as shown in FIG. 2B, when a predetermined number of infrequent luminance values occur in the luminance distribution (before generation), the luminance change determination unit 22 determines the portion of the pixel. Therefore, it can be determined that there is a high possibility that an intruder or smoke has occurred.

  The luminance change determination unit 22 performs the above-described determination individually for each predetermined pixel, and outputs the result as a map corresponding to the screen. For example, when the pixels of one screen are p × q (where p and q are integers of 2 or more), the luminance change determination unit 22 selects pixels that are likely to have intruders or smoke “1”. The other pixels are set to “0”, and each pixel of p × q is mapped or binarized to obtain a mapping image (binarized image). In addition, in comparison with the obtained histogram, when the luminance value of the corresponding pixel of the current image is a luminance value that is less than the predetermined number of times, it may be determined that the pixel has an intruder. Good.

Step 3: Function of Detection Candidate Area Identification Unit 23 The data for one screen mapped by the luminance change determination unit 22 is divided into a plurality of predetermined areas. Then, the detection candidate area specifying unit 23 determines whether or not the ratio of the pixels “1” is greater than or equal to a predetermined value for each of the previously divided areas mapped with “1” and “0”.

  FIG. 3 is an explanatory diagram showing the relationship between the area division within one screen and the mapping result within one area in Embodiment 1 of the present invention. FIG. 3A shows a plurality of divided areas set in advance in one screen. As shown in FIG. 3A, the screen is divided into a plurality of rectangular areas in a vertical and horizontal matrix. In front of the door, there is a person as an intruder, and smoke as an intruder exists under the right window glass. FIG. 3B shows a state in which each area is divided into pixel units and mapped by the luminance change determination means 22. As described above, the region is composed of a plurality of pixels.

  In FIG. 3B, the portion where the pixel is painted black means a pixel mapped to “1” as a pixel that is highly likely to have an intruder or smoke. Therefore, the detection candidate area specifying unit 23 determines the ratio of the pixels that are “1” (that is, pixels that are blacked out) for each of the divided areas based on the mapping result as illustrated in FIG. It will be determined whether or not there is a predetermined value or more.

  Next, the detection candidate area specifying unit 23 specifies an area in which the ratio of pixels that are “1” with respect to the size of one area is equal to or greater than a predetermined value as a candidate area where smoke detection should be performed in detail. On the other hand, the detection candidate area specifying unit 23 specifies an area in which the ratio of pixels that are “1” is less than a predetermined value as an area that does not perform subsequent smoke detection. Alternatively, an area in which the ratio of pixels that are “0” is equal to or greater than a predetermined value is specified as an area in which subsequent smoke detection is not performed.

  FIG. 4 is a diagram showing candidate areas identified by the smoke detection area selection unit 20 according to Embodiment 1 of the present invention. Here, the blacked-out portion is a region selected by the smoke detection area selecting unit 20, and is a region having a large ratio including the pixel “1” described above. This FIG. 4 corresponds to the state of FIG. 3A, and there are intruders in front of the door and a plurality of black areas as candidate areas under the window glass. . There are multiple areas in the image, but it is only necessary to calculate whether or not smoke is generated in that area only for such candidate areas, so that the total amount of computation can be reduced. Become. Through such a series of processes, the smoke detection area selection unit 20 selects candidate areas for which detailed smoke detection is to be performed by the subsequent smoke generation detection unit 30 from a plurality of previously divided areas for a predetermined period of time. From the calculation result of the luminance histogram, it can be specified with high accuracy. As a result, it is possible to detect smoke with high sensitivity while suppressing the influence of disturbance. In other words, if smoke or the like is generated in a certain area, most of the pixels in the area have a different distribution in the histogram and become “1”. Extracted. However, a temporary change such as illumination hardly causes different distributions in the luminance histogram, so there are few pixels that are “1”, and it is difficult to become a candidate region due to the influence of disturbance.

  Next, the function of the smoke generation detection unit 30 will be described. The smoke generation detection unit 30 can determine the presence or absence of smoke generation by extracting a feature quantity related to smoke for a candidate area where smoke detection specified by the smoke detection area selection unit 20 should be performed in detail. it can.

Step 1: Function of Smoke Feature Quantity Calculation Means 31 As typical feature quantity extraction methods, there are the following four. By applying the following method to the candidate area where smoke detection is to be performed in detail, the smoke feature quantity calculation unit 31 determines whether or not there is a high possibility that smoke has occurred in the area. Can be judged. Note that the calculation of the following smoke characteristics is a technique particularly suitable for detecting relatively slow smoke accompanied by a flow.

[Extraction method 1: Smoke detection based on luminance distribution of pixels]
The smoke feature quantity calculation means 31 calculates the luminance dispersion of the pixels in each area for each candidate area where the smoke detection specified by the smoke detection area selection unit 20 should be performed in detail. In calculating the luminance variance, the smoke feature amount calculating unit 31 does not necessarily need to use all the pixels in the region. The smoke feature amount calculating unit 31 may calculate the luminance variance only for the pixels mapped to “1” as pixels having a high possibility of intrusion or smoke by the luminance change determining unit 22.

  In addition, the smoke feature amount calculation unit 31 basically uses the latest captured image as the image for calculating the luminance dispersion. However, a plurality of captured images can be used retroactively.

  Then, the smoke feature quantity calculation means 31 determines whether or not the calculated luminance dispersion or the standard deviation obtained from the luminance dispersion is within a predetermined range. It can be determined that there is a high possibility that it has occurred.

[Extraction method 2: Smoke detection based on temporal dispersion of average luminance of pixels]
The smoke feature quantity calculation means 31 calculates the average luminance of the pixels in each area for each candidate area where the smoke detection specified by the smoke detection area selection unit 20 should be performed in detail. In calculating the average luminance, the smoke feature amount calculating unit 31 does not necessarily need to use all the pixels in the region. The smoke feature amount calculation unit 31 may calculate the average luminance only for the pixels mapped to “1” as pixels having a high possibility of occurrence of an intruder or smoke by the luminance change determination unit 22.

  Next, the smoke feature amount calculating unit 31 calculates the average luminance in the same region of a plurality of captured images going back in the past for a certain period, and generates time series data of the average luminance for each target region. . Then, the smoke feature quantity calculating means 31 calculates the luminance variance of the generated time series data of average luminance.

  Then, the smoke feature quantity calculation means 31 determines whether or not the luminance variance calculated based on the time series data of the average luminance or the standard deviation obtained from the luminance variance is within a predetermined range. If it is within the range, it can be determined that the possibility that smoke has occurred is high.

[Extraction method 3: Smoke detection based on low frequency intensity of average luminance of pixels]
The smoke feature quantity calculation means 31 generates time series data of average luminance for each target area in the same manner as the extraction method 2 described above. Then, the smoke feature amount calculation means 31 performs Fourier transform on the generated time series data of the average luminance to calculate a power spectrum.

  Next, the smoke feature quantity calculation means 31 extracts a predetermined low frequency component from the power spectrum calculated based on the time series data of the average luminance, calculates the intensity for the mode, and the intensity is a predetermined value. When it is below, it can be judged that the possibility that smoke was generated is high.

[Extraction method 4: Smoke detection based on difference average with reference image]
The smoke feature quantity calculation means 31 has, for each candidate area where smoke detection specified by the smoke detection area selection unit 20 is to be performed in detail, each pixel in the candidate area and a reference stored in the image memory 10 in advance. A luminance difference value with a corresponding pixel of the image is obtained. Further, the smoke feature quantity calculation means 31 obtains an average value of the luminance difference values for each candidate area, and the possibility that smoke has occurred when the average value is larger than a predetermined value or within a predetermined range. Can be determined to be high.

Step 2: About the function of the mapping means 32 The mapping means 32 performs mapping for each candidate area where smoke detection should be performed in detail based on the extraction results of the four types of extraction methods 1 to 4 by the smoke feature amount calculation means 31. . For example, the mapping unit 32 sets “1” for an area where it is determined that smoke is highly likely to be generated by any one or more of the four extraction methods 1 to 4, and “0” for other areas. Can be set.

  As another mapping method, the mapping means 32 sets “1” for an area where it is determined that smoke is likely to be generated by two or more types of extraction methods, and “0” for other areas. Can be set. Alternatively, an area where it is determined that there is a high possibility that smoke is commonly generated by the combination of the four extraction methods 1 to 4 is set to “1”, and the other areas are set to “0”. Can be set.

  FIG. 5 is a diagram illustrating a mapping result output by the mapping unit 32 according to the first embodiment of the present invention. By calculating the feature quantity related to smoke for the candidate area shown in FIG. 4, it is possible to select an area where smoke is likely to be generated from the candidate areas as shown in FIG. it can. In FIG. 5, the candidate area of the door portion (see FIG. 4) is an area extracted by the presence of an intruder such as a passerby that passes through the image, and it is determined that the possibility that smoke has occurred is low. , Four of the five candidate regions (see FIG. 4) under the window indicate a case where it is determined that the possibility that smoke has occurred is high.

Step 3: Function of Smoke Determining Unit 33 Based on the data of each area for one screen mapped by the mapping unit 32, the smoke determining unit 33 has a (candidate) area mapped as “1”. It is determined whether or not detection is continued for a predetermined period across the number of regions. For example, the smoke determination means 33 is a time series in which a region of n (n is an integer of 2 or more) in the vertical direction or the horizontal direction is mapped as “1” by the mapping means 32 and sequentially captured from the past to the present. When the connected area is detected continuously on the screen m (m is an integer of 2 or more) times or more, it can be finally determined that smoke is generated.

  Next, the flow of the entire process of the smoke detection device of the present invention having the configuration of FIG. 1 will be described based on the flowchart. FIG. 6 is a flowchart showing a flow of overall processing of the smoke detection device according to Embodiment 1 of the present invention.

  First, in step S601, the luminance histogram creating unit 21 generates a luminance histogram for each predetermined pixel based on a plurality of time-series image data stored in the image memory 10 (FIGS. 2A and 2B). )reference). Next, in step S602, the luminance change determination unit 22 detects the presence / absence of a luminance value newly generated by the generation of an intruder or smoke based on the generated luminance histogram, and the occurrence of the intruder or smoke is detected. Pixels that are likely to have been mapped are output (see FIG. 3B).

  Next, in step S603, the detection candidate area specifying unit 23 determines whether the ratio of pixels mapped as having a high possibility of intrusion or smoke is greater than or equal to a predetermined value for each area divided in advance. Judgment is made and a candidate area for smoke detection is specified (see FIG. 4).

  The above steps S601 to S603 are a series of processes by the smoke detection area selection unit 20. As described above, the smoke detection process performed by the smoke generation detection unit 30 is performed only on the candidate area to be subjected to the smoke detection specified by the smoke detection area selection unit 20 after step S604. For this reason, the amount of calculation can be reduced compared with the case where the whole image is processed.

  In step S604, the smoke feature amount calculating unit 31 calculates the feature amount related to smoke using only the extraction methods 1 to 4 described above for only the area specified as the candidate region where smoke detection is to be performed. Next, in step S605, the mapping unit 32 performs mapping output of an area where smoke is highly likely to be generated (see FIG. 5).

  Next, in step S <b> 606, the smoke determination unit 33 identifies an area where smoke is finally generated based on the temporal distribution and spatial distribution of the mapping result of the divided areas by the mapping unit 32.

  By performing such a series of processes, the smoke generation detection unit 30 performs the smoke detection specified by the smoke detection area selection unit 20 from the calculation result of the luminance histogram for a certain period in the past with respect to the candidate area where the smoke detection should be performed in detail. Thus, it is possible to determine the presence or absence of smoke by extracting the feature quantity related to smoke. As a result, it is possible to detect smoke with high sensitivity while suppressing the influence of disturbance.

  As described above, according to the first embodiment, a configuration is provided in which a luminance histogram within a certain past period is calculated for each predetermined pixel from a plurality of images captured in time series. As a result, it is possible to easily detect the presence or absence of a brightness value newly generated by the generation of an intruder or smoke, and to detect smoke with high sensitivity while suppressing the influence of disturbance. A detection device can be obtained.

  In particular, even when the difference in luminance between the luminance distribution before the occurrence and the luminance distribution after the occurrence is slight, it is newly generated because the luminance histogram is obtained for each pixel based on a plurality of images within a certain past period. The presence of a luminance value can be identified with high accuracy.

  Furthermore, it is possible to perform high-accuracy smoke detection that prevents erroneous detection while reducing calculation load by performing smoke detection by narrowing down to a candidate area where the presence of a newly generated luminance value is detected. .

  Furthermore, whether or not there is a high possibility that smoke is generated in the candidate area by obtaining a plurality of feature quantities as feature quantities for performing smoke detection and by a specific combination of individual judgment results based on the plurality of feature quantities Therefore, the detection accuracy can be further improved. Further, unlike the extraction method 4, the extraction methods 1 to 3 described above do not need to store a reference image in advance in calculating the feature quantity regarding smoke. Therefore, when the feature amount is obtained without using the extraction method 4, a merit that the reference image is unnecessary is also obtained.

  Further, if the area mapped as having a high possibility that smoke has been generated using the characteristic that smoke spreads over a specific area is detected continuously for a predetermined period across a certain number of areas, the smoke Smoke judging means for judging that the occurrence of smoke is provided. As a result, erroneous detection can be suppressed, and stable and highly accurate smoke detection can be realized.

  Since the present invention is configured as described above, a luminance histogram is calculated from a plurality of past images, and a luminance with a low probability is detected as an intruder, so that a region with high sensitivity can be extracted even with smoke with a small luminance difference. It is also possible to absorb influences such as changes in illumination. In addition, since the calculation of the histogram for each pixel is enormous in both the storage amount and the calculation amount, the calculation amount is reduced by sharing the histogram between adjacent pixels.

It is a block diagram of the smoke detection apparatus in Embodiment 1 of this invention. It is a figure which shows an example of the brightness | luminance histogram created by the brightness | luminance histogram creation means in Embodiment 1 of this invention. It is explanatory drawing which showed the relationship between the area | region division | segmentation in 1 screen and the mapping result in 1 area | region in Embodiment 1 of this invention. It is a figure which shows the candidate area | region specified by the smoke detection area selection part in Embodiment 1 of this invention. It is a figure which shows the mapping result output by the mapping means in Embodiment 1 of this invention. It is a flowchart which shows the flow of the whole process of the smoke detection apparatus in Embodiment 1 of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Camera, 10 Image memory, 20 Smoke detection area selection part, 21 Luminance histogram preparation means, 22 Luminance change determination means, 23 Detection candidate area | region identification means, 30 Smoke generation | occurrence | production detection part, 31 Smoke feature-value calculation means, 32 Mapping means, 33 Smoke judging means.

Claims (5)

A smoke detection device that detects the generation of smoke by performing image processing on an image captured by a surveillance camera,
An image memory for storing a plurality of images taken in time series by the monitoring camera;
Based on the plurality of images stored in the image memory, a luminance histogram of the same pixel is calculated for each predetermined pixel over a plurality of times within a past fixed period, and an intruder or smoke is generated based on the luminance histogram. A smoke detection area selecting unit that detects presence / absence of a newly generated luminance value and identifies a candidate area to be subjected to the image processing. The smoke detection device according to claim 1,
The smoke detection area selection unit is
A luminance histogram creating means for calculating the luminance histogram;
Based on the luminance histogram individually calculated for each of the predetermined pixels by the luminance histogram creating means, the presence / absence of a luminance value newly generated by the occurrence of an intruder or smoke is detected, and the newly generated A luminance change determination means for mapping a pixel having a luminance value;
For each predetermined area composed of a plurality of pixels, the luminance change determination means obtains a ratio of pixels mapped as having the newly generated luminance value, and the ratio is equal to or greater than a predetermined value. A smoke detection device comprising: a detection candidate region specifying unit that specifies a region as the candidate region to which the image processing is performed. The smoke detection device according to claim 1 or 2,
A smoke generation detection unit that calculates a feature quantity related to smoke for each area identified as the candidate area in the image captured by the monitoring camera, and determines whether smoke is generated based on the calculation result A smoke detection device comprising: The smoke detection device according to claim 3,
The smoke generation detection unit,
For each of the candidate areas, a smoke feature quantity calculating unit that extracts a feature quantity related to smoke and determines whether or not smoke is highly likely based on the extraction result;
Mapping means for mapping an area where it is determined that smoke is likely to be generated by the smoke feature quantity calculation means;
Smoke determination that determines that smoke has been generated when the mapping area is determined to have a high possibility that smoke has been generated and is continuously detected for a predetermined period across a predetermined number of areas. And a smoke detector. The smoke detection device according to claim 4,
The smoke feature quantity calculation means extracts a plurality of feature quantities as the feature quantity in order to determine whether or not there is a high possibility that smoke has occurred,
The mapping means maps a region where it is determined that there is a high possibility that smoke is commonly generated in the combination by a specific combination of individual determination results based on the plurality of feature amounts. Detection device.

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