JP6835079B2 - Monitoring system - Google Patents

Description

The present invention relates to a monitoring system that monitors an object by scanning and projecting, for example, laser light.

As a monitoring device for detecting an intruder or the like into the monitoring space, a device using a distance image has been proposed. Here, the distance image has distance information as a pixel value. Specifically, as shown in Patent Document 1, a monitoring that sends a laser beam or the like toward a monitoring space and measures the distance from the transmission to the reception of reflected light to an object in the monitoring space. The device is known. In such a monitoring device, distance information regarding a plurality of directions facing the monitoring space can be obtained by sequentially changing the transmission direction of a measurement medium such as a laser beam and scanning the inside of the monitoring space two-dimensionally. A distance image can be formed.

In a monitoring device using a distance image, a distance image (background image) that is a background in which no moving object exists is obtained in advance, and the obtained background image is compared with the input distance image (current image) to obtain a predetermined value. The so-called background subtraction method is used, in which pixels whose distances have changed are extracted to obtain a change region. Thereby, it is possible to determine whether or not the moving object is the target object to be detected based on the size and shape of the changing region and the distance information in the current image.

The distance image has information of the direction of the object as seen from the transmission / reception unit such as the laser luminous flux and the distance to the object. Therefore, the size and shape of an object can be known from a distance image. For example, in the application of intruder detection, it is possible to distinguish between a relatively large person in the distance and a small animal in the vicinity (rat, cat, etc.). This makes it possible to improve the detection accuracy of intruders.

JP-A-2007-122507 Japanese Unexamined Patent Publication No. 2011-133320

Here, when the object is detected by the object detection algorithm using the background subtraction method, the variation in the measurement distance becomes a problem. In the background subtraction method, background distance data is held as a reference image, and if the distance difference value from the current image is equal to or greater than a predetermined value, the pixel is regarded as a foreground object located in front of the reference image, that is, a detected object. However, when the reproducibility of distance measurement is low and the measurement distance variation is large, the background itself may be erroneously detected as an object due to the distance variation in the background due to the measurement distance variation.

This problem will be described more specifically. FIG. 4 shows an example of distribution of distance variation within a predetermined period of a certain pixel. For example, when the average value of a predetermined number of frames is adopted as the background pixel value of the pixel, if a distance value smaller than the average value is acquired due to the variation in the measurement distance in the subsequent measurement, the pixel is used in the frame. The background is extracted as a detection object by the background subtraction method, resulting in false detection. This is a problem especially in the case of an example having a large distance variation as shown in FIGS. 4 (b) to 4 (d).

For example, the following can be considered as countermeasures for the above problems. Normally, it is unlikely that the distance variation becomes large only in a specific portion in the frame, so that the false detection pixels due to the measurement distance variation as described above are often scattered at discrete positions in the frame. By utilizing this property, erroneous detection can be reduced by targeting only a mass having a certain number of pixels or more as a detection target. However, this countermeasure makes it impossible to detect small objects with a number of pixels less than a certain number, so there remains the problem that it becomes difficult to detect small flying objects such as drones and thrown objects.

On the other hand, Patent Document 2 describes a distance image sensor that scans light on a predetermined object to be measured to detect the distance value of each pixel in this scanning region, and a distance value of each pixel input from the distance image sensor. An image data processing device including an image processing circuit that generates a background image based on the above and also generates a distance image from the difference in the distance value of the background image, and the image processing circuit is provided by the distance image sensor. A distance image processing system configured to store a distance value that maximizes the acquired distance value of each pixel and generate a background image based on the maximum distance value of each pixel is disclosed. ..

However, although this prior art is effective for the pixels as shown in FIG. 4 (a) in which the distance variation falls within the range assumed in advance, the prior art as shown in FIGS. 4 (b) to 4 (d) is effective. For pixels with a large distance variation that cannot be expected, there is a problem that each time a distance outside the range of the distance variation assumed in advance is measured, the pixel is extracted as a detection object and becomes a false detection pixel. there were.

The present invention has been made in view of the above circumstances, and monitoring that reduces false detection of object detection by the background subtraction method by setting an appropriate background pixel value according to the distribution characteristics of measurement distance variation of each pixel. The purpose is to provide a system.

In order to achieve at least one of the above mentioned objectives, a monitoring system that reflects one aspect of the invention
A light emitting / receiving unit including an emitting unit that emits a light beam, a scanning unit that scans the light beam in the monitoring space, and a light receiving unit that receives the light beam reflected from an object in the monitoring space.
It has a processing unit that obtains a distance value to the object based on the time difference between the emission time when the luminous flux is emitted from the emission unit and the light reception time when the light flux reflected from the object is received by the light receiving unit. It ’s a monitoring system,
As a pre-monitoring process, the processing unit performs n times of scanning to obtain n distances from the light emitting / receiving unit to the object, and performs statistical processing. If the standard deviation σ is less than the threshold value σth, the processing unit performs statistical processing. The median value of the n distance values is used as the background pixel value, and when the standard deviation σ is equal to or greater than the threshold value σth, the background is a value lower than the median value of the n distance values and equal to or greater than the minimum value. As a pixel value
At the time of object monitoring, the processing unit measures the reflected light from the monitoring space to obtain a distance value, and the obtained distance value measures the reflected light in the same light emitting / receiving direction to obtain the background. When it is smaller than the pixel value, it is recognized as a monitored object.

According to the present invention, it is possible to provide a monitoring system that reduces false detection of object detection by the background subtraction method by setting an appropriate background pixel value according to the distribution characteristic of the measurement distance variation of each pixel.

It is sectional drawing of the monitoring apparatus MD which concerns on this embodiment. It is a figure which shows the state which scans in the monitoring space of the monitoring apparatus MD by the laser spot light SB (shown by a hatching) which emits with the rotation of a mirror unit MU. It is a figure which shows the state which looked at the monitoring space from the side. This is an example of a histogram in which the distance value to the background object on one pixel obtained by n times of scanning the background object is taken on the horizontal axis and the frequency is shown on the vertical axis. It is a flowchart which shows the monitoring preprocessing for the processing circuit PROC to generate a reference image. It is a histogram which shows the experimental result performed by the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. FIG. 1 is a cross-sectional view of a monitoring device MD as a monitoring system according to the present embodiment, but the shape and length of the components may differ from the actual ones.

The monitoring device MD is, for example, a pulse type semiconductor laser LD that emits a laser beam, a collimating lens CL that converts divergent light from the semiconductor laser LD into parallel light, and a laser beam that is parallel to the collimating lens CL. A mirror unit MU that scans and projects light toward the monitoring space by a rotating mirror surface and reflects the reflected light from the object, and a lens LS that collects the reflected light from the object reflected by the mirror unit MU. , The photodiode PD that receives the light collected by the lens LS, the processing circuit (processing unit) PROC that obtains the distance information according to the time difference between the emission timing of the semiconductor laser LD and the reception timing of the photodiode PD, and the mirror. It has a motor MT for rotationally driving the unit MU and a housing CS for accommodating them. The photodiode PD has a plurality of pixels arranged in the Z direction.

In the present embodiment, the semiconductor laser LD and the collimating lens CL form an emitting unit LPS, the lens LS and the photodiode PD form a light receiving unit RPS, and the mirror unit MU constitutes a scanning unit, and these are used for throwing. It constitutes a light receiving unit. It is preferable that the optical axes of the emitting unit LPS and the light receiving unit RPS are orthogonal to the rotation axis RO of the mirror unit MU.

The box-shaped housing CS fixed to a rigid wall WL or the like has an upper wall CSa, a lower wall CSb facing the upper wall CSa, and a side wall CSc connecting the upper wall CSa and the lower wall CSb. An opening CSd is formed in a part of the side wall CSc, and a transparent plate TR is attached to the opening CSd.

The mirror unit MU has a shape in which two quadrangular pyramids are joined in opposite directions and integrated, that is, four pairs (but not limited to four pairs) of mirror surfaces M1 and M2 tilted in a pair facing direction. ) Have. The mirror surfaces M1 and M2 are preferably formed by depositing a reflective film on the surface of a resin material (for example, PC) in the shape of a mirror unit.

The mirror unit MU is connected to the shaft MTa of the motor MT fixed to the housing CS and is rotationally driven. In the present embodiment, the axis (rotational axis) of the axis MTa extends in the Z direction, which is the vertical direction, and the XY plane formed by the X and Y directions orthogonal to the Z direction is the horizontal plane. The axis of MTa may be tilted with respect to the vertical direction.

Next, the object detection principle of the monitoring device MD will be described. In FIG. 1, the divergent light emitted intermittently in a pulse shape from the semiconductor laser LD is converted into a parallel light beam by the collimating lens CL, is incident on the first mirror surface M1 of the rotating mirror unit MU, and is reflected here. After being further reflected by the second mirror surface M2, the light is scanned and projected as laser spot light having a vertically long rectangular cross section, for example, through the transparent plate TR and directed toward the external monitoring space. The direction in which the emitted laser spot light is reflected by the object and returned as reflected light is called the light emitting / receiving direction. The laser spot luminous flux traveling in the same light emitting / receiving direction is detected by the same pixel.

FIG. 2 is a diagram showing a state in which the laser spot light SB (shown by hatching) emitted in response to the rotation of the mirror unit MU scans in the monitoring space of the monitoring device MD. Here, the crossing angles are different in the combination of the first mirror surface M1 and the second mirror surface M2 of the mirror unit MU. The laser beam is sequentially reflected by the rotating first mirror surface M1 and the second mirror surface M2. First, the laser beam reflected by the first pair of first mirror surfaces M1 and the second mirror surface M2 horizontally moves from left to right in the uppermost region Ln1 of the monitoring space according to the rotation of the mirror unit MU. Is scanned. Next, the laser beam reflected by the second pair of the first mirror surface M1 and the second mirror surface M2 horizontally covers the second region Ln2 from the top of the monitoring space from the left in accordance with the rotation of the mirror unit MU. Scanned to the right. Next, the laser beam reflected by the third pair of the first mirror surface M1 and the second mirror surface M2 horizontally covers the third region Ln3 from the top of the monitoring space from the left in accordance with the rotation of the mirror unit MU. Scanned to the right. Next, the laser beam reflected by the 4th pair of the first mirror surface M1 and the second mirror surface horizontally moves from left to right in the lowermost region Ln4 of the monitoring space according to the rotation of the mirror unit MU. It is scanned. This completes one scan of the entire monitoring space that can be monitored by the monitoring device MD. One frame FL can be obtained by combining the images obtained by scanning the regions Ln1 to Ln4. Then, if the first pair of first mirror surfaces M1 and the second mirror surface M2 return after one rotation of the mirror unit MU, the area from the top region Ln1 to the bottom region Ln4 of the monitoring space is restored again. The scanning is repeated to obtain the next frame FL.

In FIG. 1, a part of the laser beam reflected by hitting an object among the light beams scanned and projected is transmitted through the transparent plate TR again and incident on the second mirror surface M2 of the mirror unit MU in the housing CS. Here, it is reflected, further reflected by the first mirror surface M1, collected by the lens LS, and detected for each pixel on the light receiving surface of the photodiode PD. Further, the processing circuit PROC, which is a processing unit, obtains distance information according to the time difference between the emission timing of the semiconductor laser LD and the light receiving timing of the photodiode PD. As a result, the object can be detected in the entire area in the monitoring space, and a frame FL (see FIG. 2) as a distance image having distance information for each pixel can be obtained. Such a distance image can be transmitted to a distant monitor via a network (not shown) or the like for display, or can be stored in a storage device.

Next, the detection algorithm of the monitored object of the monitoring device MD using the background subtraction method will be described. FIG. 3 is a diagram showing a state in which the monitoring space is viewed from the side. In the background subtraction method adopted in this embodiment, a reference image (also referred to as a background image) acquired in advance is used. Specifically, as a pre-preparation (pre-processing) for monitoring, as shown in FIG. 3A, the laser spot luminous flux SB is scanned from the monitoring device MD in the absence of moving objects such as humans and animals. As a result, a reference image (see FIG. 2) can be obtained based on the reflected light RB1 obtained from the background object.
The details of the reference image forming method will be described later.

In the actual monitoring, as shown in FIG. 3B, when the intruder OBJ appears in front of the background object, the reflected light RB2 from the intruder OBJ is newly generated. The processing circuit PROC can compare the reference image of FIG. 3 (a) with the distance image of FIG. 3 (b), and if there is a difference, it can call attention to the fact that some object has appeared. Further, when the intruder OBJ is moving, its position changes in the frame obtained by repeating the scanning, so that the processing circuit PROC tracks the intruder OBJ and obtains the moving direction and speed (moving object detection is performed). )be able to.

Next, problems in acquiring a reference image will be described. When the mirror unit MU is rotated to scan the laser luminous flux as in the monitoring device MD of the present embodiment, it is inevitable that the incident position on the background object varies due to slight deflection of the mirror unit MU or the like. ..
Therefore, the laser luminous flux is scanned on the background object a plurality of times (n times, where n is an integer of 2 or more), the average value is taken for each pixel, and the distance value is averaged. However, depending on the background object, it may not be appropriate to average the distance values obtained by a plurality of scans. An example thereof will be described below.

FIG. 4 is an example of a histogram showing the distance value to the background object on one pixel obtained by n times of scanning the background object on the horizontal axis and the frequency thereof on the vertical axis. First, when the background object is the white wall surface of the building BL in FIG. 2 (incident position P1 of the laser luminous flux), the reflection conditions are uniform because it is a flat surface having substantially the same reflectance, and the distance value is obtained for each scan. The data obtained as shown in FIG. 4A almost follow a normal distribution. Pixels having substantially the same distance value in this way are referred to as flat pixels here.

On the other hand, when the background object is a tree or the like in FIG. 2 (incident position P2 of the laser luminous flux), the reflection conditions are not stable due to unevenness or local color change, so based on the reflected light. When the distance value is obtained, the obtained data tends to vary as shown in FIG. 4 (b). Pixels in which the distance value varies relatively with each scan are referred to as miscellaneous pixels here.

Further, when the background object is the edge of the building BL in FIG. 2 (incident position P3 of the laser light beam), the laser spot light beam hits the building BL at one time due to the runout of the mirror unit MU, and at another time, the building BL It deviates from and hits the tree in the back. In such a case, as shown in FIG. 4C, two groups of data (that is, a distance value to a building or a distance value to a tree) can be obtained for each scan on the same pixel. Pixels having two groups of distance values in a plurality of scans in this way are referred to as scrap pixels here.

Further, when the background object is a black painted part of the building BL in FIG. 2 (incident position P4 of the laser light flux), the reflected light can be detected by the same pixel of the photodiode PD depending on the irradiation condition of the laser spot light flux. In some cases, it cannot be detected. If it cannot be detected, it will be treated as an unmeasurable point, and the distance value will be unclear. In such a case, as shown in FIG. 4D, two groups of data (that is, a distance value to a building or an unmeasurable point) can be obtained for each scan on the same pixel. In this way, a pixel whose distance value is unknown depending on scanning is referred to as a last-minute pixel here.

Here, in the case of the flat pixel showing the data in FIG. 4A, since there is little variation in the data, the median value C can be set as the maximum distance value (also referred to as the background pixel value) of the background object, for example, the median value. By setting a predetermined dead zone (for example, ± 18 cm) before and after C, there is no possibility of detecting the background object itself as a monitoring object, so that there is no particular problem. On the other hand, in the case of the miscellaneous pixels showing the data in FIG. 4B, the scraped pixels showing the data in FIG. 4C, and the last-minute pixels showing the data in FIG. 4D, what is the maximum distance value of the background object? The question is whether to set it.

For example, in the miscellaneous pixels shown in FIG. 4B, if the average value C of the data is set as the maximum distance value, the distance value (for example, peak PK1, that is, the background object) exceeds the predetermined dead zone from the average value C in a certain frame. If the object itself) is obtained, it may be erroneously detected as a monitored object. Similarly, in miscellaneous pixels, if the maximum value MX of the data is set as the maximum distance value, a distance value (for example, peak PK1 or the background object itself) can be obtained from the maximum value MX in a certain frame beyond a predetermined dead zone. If this happens, it may be erroneously detected as a monitored object.

Further, in the scraped pixel shown in FIG. 4C, if the average value C of the data is set as the maximum distance value, the distance value (for example, peak PK2, that is, the background object) exceeds the predetermined dead zone from the average value C in a certain frame. If the object itself) is obtained, it may be erroneously detected as a monitored object. Similarly, if the maximum value MX of the data is set as the maximum distance value in the Kasuri pixel, the distance value (for example, peaks PK2, PK3, that is, the background object itself) is set in front of the maximum value MX beyond a predetermined dead zone in a certain frame. If obtained, it may be erroneously detected as a monitored object.

Further, in the last-minute pixels shown in FIG. 4D, if the average value C of the data is set as the maximum distance value, the distance value (for example, peak PK4, that is, the background object) exceeds the predetermined dead zone from the average value C in a certain frame. If the object itself) is obtained, it may be erroneously detected as a monitored object. Similarly, if the maximum value MX of the data is set as the maximum distance value in the last-minute pixel, a distance value (for example, peak PK4, that is, the background object itself) is obtained from the maximum value MX in a certain frame beyond a predetermined dead zone. If this happens, it may be erroneously detected as a monitored object.

In the present embodiment, the standard deviation σ is used to separate the flat pixel from the other pixels, and then the maximum distance value is set. Specifically, the distance value data obtained in n scans is statistically processed, and if the standard deviation σ is smaller than the threshold value σth, it is determined that the data is a flat pixel with relatively little variation in the data. If it is a flat pixel, the median value C of the data can be used as the maximum distance value as described above. On the other hand, if the standard deviation σ is equal to or greater than the threshold value σth, it is determined that the data variation is relatively large and that the pixels are other than flat pixels, that is, miscellaneous pixels, scraped pixels, or barely pixel.

Further, when it is determined that the pixel is miscellaneous pixel, scraped pixel, or barely pixel, the value MN (minimum value) closest to the monitoring device MD among the distance values obtained in n scans is set to the maximum distance value of the pixel. And. By doing so, when monitoring an object using the background subtraction method, if a distance value larger than the maximum distance value is obtained, a negative value can be obtained by taking the difference. False detection can be suppressed by ignoring the detection result in the object detection algorithm. As a result, appropriate object detection can be performed. When it is determined that the pixel is miscellaneous pixel, scraped pixel, or barely pixel, the minimum value MN among the distance values obtained in n scans is set as the maximum distance value of the pixel, but the present invention is limited to this. However, if the value is lower than the median C and greater than or equal to the minimum value MN, the effect can be obtained. Further, the median value may be within ± 10% of the variation width with respect to the average value of the distance values obtained by calculation.

FIG. 5 is a flowchart showing a monitoring pre-processing for the processing circuit PROC to generate the reference image described above. Here, the photodiode PD used is one in which six pixels are arranged in a row, but the present invention is not limited to this, and for example, one or a plurality of pixels may be used. First, by rotating the mirror unit MU n times while irradiating the background object with the laser spot luminous flux, the distance values (sample data group) for n frames are collected and stored in a memory (not shown). And.

In step S101 of FIG. 5, the processing circuit PROC is set to m = 1 in order to obtain the maximum distance value for the first pixel. Next, in step S102, the processing circuit PROC reads out n distance values in the m = 1st pixel from the stored sample data group, performs statistical processing, and obtains the standard deviation σ. Further, in step S103, the processing circuit PROC compares the standard deviation σ with the threshold σth. Here, when it is determined that the standard deviation σ is smaller than the threshold value σth, the processing circuit PROC sets the median value of the sample data group as the maximum distance value in the first pixel. On the other hand, when it is determined that the standard deviation σ is equal to or greater than the threshold value σth, the processing circuit PROC sets the minimum value of the sample data group as the maximum distance value in the first pixel.

After that, unless it is determined to be the last pixel in step S106, the processing circuit PROC sets m = m + 1 in step S107 and returns the flow to step S102 again in order to obtain the maximum distance value for the next pixel. By repeating the above, the maximum distance value can be obtained for the m = 2, 3, 4, ... Th pixel. On the other hand, if it is determined in step S106 that the maximum distance value has been obtained for the last (m = N) pixel, it means that the maximum distance value has been obtained for all the pixels, so that the processing circuit PROC completes the process. A reference image can be obtained from the maximum distance value obtained for each pixel as described above.

The results of experiments conducted by the present inventor will be described below. FIG. 6 is a histogram showing the results of experiments conducted by the present inventor, focusing on the data of a specific pixel when scanning a plurality of times, and shows the frequency on the vertical axis and the distance value on the horizontal axis. ing. However, the numerical value of the distance value is relative.

FIG. 6A shows a laser spot luminous flux irradiating the center of a fixed plate having uniform reflectance and no unevenness, and processing the distance value of a specific pixel that receives the reflected light. This is an example of flat pixels. When irradiating a fixed plate with uniform reflectance and no unevenness with a laser spot luminous flux, the distance value based on the reflected light should be constant because it is not easily affected by the runout of the mirror unit. Actually, the distance value changes slightly due to the influence of electrical noise and the like. However, when the standard deviation σ is obtained by statistical processing, it can be seen that the value becomes small and the variation is small. In this example, the standard deviation was σ = 0.475.

FIG. 6B shows a laser spot light beam irradiating the vicinity of a boundary portion of materials having different reflectances and processing the distance value of a specific pixel that receives the reflected light, and the specific pixel becomes a miscellaneous pixel. This is an example. As is clear from comparison with FIG. 6A, the variation in the distance value is increased as compared with the flat pixel. Therefore, when the standard deviation σ was obtained by statistical processing, the standard deviation σ = 0.985.

FIG. 6C shows a switch string of a lighting fixture hung on the ceiling is irradiated with a laser spot luminous flux, and the distance value of a specific pixel that receives the reflected light is processed, and the specific pixel is a scrap pixel. This is an example. As is clear from FIG. 6 (c), the distance values of the two groups were obtained. Therefore, when the standard deviation σ was obtained by statistical processing, the standard deviation σ = 68.84.

FIG. 6D shows a floor surface whose reflected light intensity is near a measurable level is irradiated with a laser spot luminous flux, and the distance value of a specific pixel that receives the reflected light is processed. This is an example of a last-minute pixel. As is clear from FIG. 6D, in one scan, the reflected light from the floor surface is above the measurable level and a distance value is obtained, but in another scan, the reflected light from the floor surface is below the measurable level. The distance value indicates the unmeasurable point (infinity on the detection algorithm). Therefore, when the standard deviation σ was obtained by statistical processing, the standard deviation σ = 30166.42.

From the above experimental results, it was found that whether a specific pixel is a flat pixel or a other pixel can be determined by comparing the standard deviation σ with the threshold value σth. In the above example, it is the miscellaneous pixels that have the closest standard deviation to the flat pixels. Therefore, by setting the threshold value σth to 0.7, it is possible to determine whether the specific pixel is a flat pixel, a miscellaneous pixel, or a other pixel.

The present invention is not limited to the embodiments / examples described in the present specification, and the inclusion of other embodiments / examples / modifications is based on the embodiments and technical ideas described in the present specification. It is obvious to those skilled in the art. The description and examples of the specification are for illustration purposes only, and the scope of the present invention is indicated by the claims described below.

CL collimating lens CS housing CSa upper wall CSb lower wall CSc side wall CSd opening FL frame FS fence G ground LD semiconductor laser LPS exit LS lens M1, M2 mirror surface MD monitoring device MT motor MTa axis MU mirror unit OBJ intruder PD photo Diode PROC Processing circuit RB1, RB2 Reflected light RO Rotating axis RPS Light receiving part SB Laser spot light TR Transparent plate WL Wall

Claims (3)

A light emitting / receiving unit including an emitting unit that emits a light beam, a scanning unit that scans the light beam in the monitoring space, and a light receiving unit that receives the light beam reflected from an object in the monitoring space.
It has a processing unit that obtains a distance value to the object based on the time difference between the emission time when the luminous flux is emitted from the emission unit and the light reception time when the light flux reflected from the object is received by the light receiving unit. It ’s a monitoring system,
As a pre-monitoring process, the processing unit performs n times of scanning to obtain n distances from the light emitting / receiving unit to the object, and performs statistical processing. If the standard deviation σ is less than the threshold value σth, the processing unit performs statistical processing. The median value of the n distance values is used as the background pixel value, and when the standard deviation σ is equal to or greater than the threshold value σth, the background is a value lower than the median value of the n distance values and equal to or greater than the minimum value. As a pixel value
At the time of object monitoring, the processing unit measures the reflected light from the monitoring space to obtain a distance value, and the obtained distance value is obtained by measuring the reflected light in the same light emitting / receiving direction. A monitoring system that recognizes an object to be monitored when it is smaller than the pixel value. The monitoring system according to claim 1, wherein when the standard deviation σ is equal to or greater than the threshold value σth, the processing unit uses the minimum value of the n distance values as the background pixel value. The scanning unit reflects the luminous flux emitted from the emitting unit toward the monitoring space, and reflects the reflected light returned from the monitoring space to cause the light receiving unit to receive the light. The monitoring system according to claim 1 or 2.

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