JP6739119B1 - Risk judgment program and system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a risk discriminating program and system for detecting a risk in advance and preventing an accident. SOLUTION: In a risk level determination program for determining the risk level of a suspicious person intruding into a building structure, there are three levels of risk levels for reference image information P11, 12, 13 taken outside the building structure. The above-mentioned degree of association is acquired in advance, and the degree-of-relationship acquisition step of acquiring the reference structure information F, G, and H including the structural information about the building structure, and a new building when the risk is newly determined. The image information is acquired by photographing the outside of the structure, and the information acquisition step of acquiring the structure information of the building structure and the degree of association acquired in the degree of association acquisition are used and linked to the structure information. The reference|standard structure information F, G, H is referred, and the determination step which determines the risk degree of a suspicious person invading a building structure based on the image information acquired through the information acquisition step. [Selection diagram] Figure 10

Description

The present invention relates to a risk determination program and system for determining the risk of a suspicious person entering a building structure.

BACKGROUND ART Heretofore, various attempts have been made to prevent a suspicious person from invading a building or a building structure such as a building in which a company resides. In particular, since single-family building structures have more intrusion routes than apartments, it is necessary to take stronger measures against suspicious person intrusion to protect the safety of residents.

Japanese Patent Application No. 2016-210848

In the disclosed technology of Patent Document 1 described above, a technology of identifying a person from a face image by artificial intelligence is described, but the artificial intelligence is utilized to quantitatively determine the risk of suspicious person intrusion into an actual building structure. It cannot be judged. By actually predicting the degree of danger with high accuracy, when the resident is in danger, he/she will contact the security company and the police as soon as possible. It was necessary to prevent unintentional mobilization of guards and police officers by security companies. Such a point of view is not specifically described in the disclosed technique of the cited document 1.

Therefore, the present invention has been devised in view of the above-described problems, and an object thereof is to detect the risk of a suspicious person invading a building structure in advance and protect the safety of the occupants. It is therefore an object of the present invention to provide a risk discriminating program and system for automatically discriminating a risk using artificial intelligence.

The risk level determination program according to the present invention is a risk level determination program for determining the risk level of a suspicious person entering a building structure, and the risk level for reference image information of the outside of the building structure is 3 In addition to the step of acquiring the degree of association in advance or higher, and the step of obtaining the degree of association for acquiring the reference structural information including the structural information about the building structure, and newly determining the degree of risk, the external structure of the building structure is newly added. While acquiring the image information by capturing the image, the information acquisition step of acquiring the structure information of the building structure and the association degree acquired in the association degree acquisition step are used, and the reference is linked to the structure information. It is characterized in that the computer is caused to execute a determination step of determining the risk level of a suspicious person entering the building structure based on the image information acquired through the information acquisition step by referring to the structure information.

The risk level determination system according to the present invention is a risk level determination system for determining the risk level of a suspicious person entering a building structure, and the risk level for reference image information of the outside of the building structure is 3 In addition to previously obtaining the degree of association of more than one stage, and the degree of association acquiring means for obtaining the reference structural information including the structural information about the building structure, and newly determining the degree of danger Image information is acquired by capturing the image, and the information acquisition unit that acquires the structural information of the building structure and the association degree acquired by the association degree acquisition unit are used, and a reference that links to the structural information is used. It is characterized by further comprising: discrimination means for discriminating a risk level of a suspicious person intruding into the building structure based on the image information acquired through the information acquisition means by referring to the construction structure information.

In order to detect the risk of a suspicious person invading a building structure in advance and prevent the incident from occurring, the risk can be automatically and highly accurately determined using artificial intelligence.

It is a block diagram which shows the whole structure of the risk determination system in which the risk determination program to which this invention is applied is mounted. It is a figure which shows the specific structural example of a discrimination device. It is a figure for demonstrating operation|movement in the risk determination program to which this invention is applied. It is a figure which shows the example which set the connection degree of 3 steps or more including the reference location information. It is a figure which shows the example which set the degree of association of three steps or more including the in-home reference pattern information. It is a figure which shows the example which set the connection degree of 3 steps or more including the reference time zone information. It is a figure which shows the example which set the connection degree of 3 steps or more including the reference person information. It is a figure which shows the example which set the connection degree of 3 steps or more including the reference audio|voice information. It is a figure which shows the example which set the connection degree of three steps or more including the security service for reference. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention. It is a figure for demonstrating operation|movement of this invention.

Hereinafter, a risk level determination program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a risk level determination system 1 in which a risk level determination program according to the present invention is installed. The risk determination system 1 includes an information acquisition unit 9, a determination device 2 connected to the information acquisition unit 9, and a database 3 connected to the determination device 2.

The information acquisition unit 9 is a device for a person who uses the system to input various commands and information, and is specifically configured by a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be a device such as a microphone capable of detecting a voice and converting the voice into text information. In addition, the information acquisition unit 9 may be configured as an imaging device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be composed of a scanner having a function of recognizing a character string from a paper document. The information acquisition unit 9 may be integrated with the determination device 2 described below. The information acquisition unit 9 outputs the detected information to the determination device 2.

The database 3 accumulates information about the risk of suspicious person intrusion into a building structure, such as a case that occurred before the suspicious person invaded the building structure, or a case where the risk was high even though the incident did not occur. doing. The database 3 includes reference image information previously captured by a camera that actually constitutes the information acquisition unit 9, reference structure information including structure information regarding the building structure, and a reference location for identifying the location of the building structure. By analyzing information, reference in-home pattern information indicating the in-home pattern of the resident of the building structure, reference time zone information indicating the time zone at the time of capturing the reference image information, and reference image information The extracted reference person information, reference voice information recorded at the time of shooting the reference image information, reference security service information indicating the contract status of the security service of the building structure security company, and the like are accumulated. In addition to this, the database 3 stores reference traffic information indicating the traffic volume of roads around the building structure, and reference crime history information regarding past crime history in the area of the building structure. There is. The term “building structure” includes not only houses such as single-family homes, condominiums, condominiums, and apartments, but also buildings, schools, hospitals, public facilities, amusement facilities, etc. where the company resides.

The discriminating device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet type terminal, a wearable terminal, etc. in addition to the PC. It may be a materialized product. The user can determine the degree of risk of a suspicious person invading the building structure by obtaining the search solution by the determination device 2.

FIG. 2 shows a specific configuration example of the discrimination device 2. The discriminating device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire discriminating device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard or the like. A communication unit 26, a determination unit 27 that makes various determinations, and a storage unit 28, which is represented by a hard disk or the like and stores a program for performing a search to be executed, are connected to the internal bus 21. .. Further, the internal bus 21 is connected to a display unit 23 as a monitor that actually displays information.

The control unit 24 is a so-called central control unit for controlling each component installed in the discrimination device 2 by transmitting a control signal via the internal bus 21. The control unit 24 also transmits various control commands via the internal bus 21 in response to an operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input by the user. When the user inputs the execution command, the operation unit 25 notifies the control unit 24 of the input. The control unit 24, which has received this notification, executes the desired processing operation in cooperation with each component including the determination unit 27. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The determination unit 27 is responsible for various determinations regarding the risk of a suspicious person entering a building structure. The determination unit 27 reads various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The determination unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any known artificial intelligence technology.

The display unit 23 is configured by a graphic controller that creates a display image under the control of the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is configured by a hard disk, under the control of the control unit 24, predetermined information is written to each address and is read out as needed. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation of the risk determination system 1 having the above-described configuration will be described.

In the risk discriminating system 1, it is premised that a combination of reference image information and reference structure information is formed as shown in FIG. 3, for example. Reference image information is a camera installed on a building structure, gate, entrance, garden, exterior, fence, fence, parking space, doorway of the building structure, etc. The image is, for example, an image of a passerby passing by a road outside the building structure, an image of a resident of the building structure entering the building structure through a gate, and a suspicious person There are various images, such as an image of climbing a fence of a thing and trying to invade it. This image may be either a still image or a moving image.

The reference structure information is data indicating the structure of the building structure. For example, a photograph of the entire building structure taken from the air, a photograph of the building structure taken from the road side, and electronic data of a completed drawing of the building structure. , Electronic data such as a structural drawing of the entire building structure shown on the map.

In the example of FIG. 3, the reference image information is, for example, reference image information P11 to P13 obtained by photographing the outside of the building structure for each place or for each time series, and as the reference structure information, structures F to structures. I and so on.

As the input data, such reference image information and reference structure information are arranged. A combination of the reference image information as the input data and the reference structure information is the intermediate node shown in FIG. Each intermediate node is further connected to an output. In this output, the danger level of a suspicious person entering a building structure as an output solution is displayed as a percentage.

Each combination (intermediate node) of the reference image information and the reference structure information is associated with each other through the degree of association of three or more levels with respect to the risk as the output solution. The reference image information and the reference structure information are arranged on the left side via the degree of association, and the respective risk degrees are arranged on the right side via the degree of association. The degree of association indicates which degree of risk is highly relevant to the reference image information and the reference structure information arranged on the left side. In other words, this degree of association is an index that indicates what degree of danger each of the reference image information and the reference structure information is likely to be associated, and is the most important from the reference image information and the reference structure information. It shows the accuracy in selecting a certain risk level. In the example of FIG. 3, w13 to w22 are shown as the degree of association. These w13 to w22 are shown in 10 levels as shown in Table 1 below, and the closer to 10 points, the higher the degree of association between each combination as an intermediate node and the degree of danger as an output. On the contrary, the closer to one point, the lower the degree of association between each combination as an intermediate node and the degree of danger as an output.

The determination device 2 acquires in advance the association degrees w13 to w22 of three or more levels shown in FIG. In other words, the discrimination device 2 accumulates the reference image information, the reference approach information, and the data indicating the degree of danger in that case in analyzing the actual degree of danger, and analyzes these. , By analyzing, the association degree shown in FIG. 3 is created.

For example, it is assumed that the reference image information P11 is an image in which a resident of the building structure enters the building structure through the gate. Further, when it is assumed that the structure F is the structure of the object to be discriminated, whether or not a case in which damage to the resident of the structure has occurred in the previous data, or even if the case does not occur It is extracted whether or not there was a degree of danger that an incident would occur and whether or not it was particularly safe. These data can be obtained from the record of the image data taken by the past camera for the reference image information, and the reference structure information is the completion drawing and the map data recorded in the house maker or the government office, Alternatively, the image may be extracted from an aerial image or image data of a building structure previously imaged from a road. The digitization of the risk level may be performed by visually recognizing the reference image information by a plurality of people and totaling a questionnaire survey or the like regarding the risk level.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P11 and the structure F, past data such as whether or not an incident actually occurred, or whether or not the incident was not reached but the risk was high, etc. Analyze from. The higher the degree of association that leads to high-risk output, the higher the number of cases in which an incident occurs, and the higher the risk of an incident, and the higher the degree of association that leads to low-risk output, as the number of cases in which an incident occurs. Set. In the example of the intermediate node 61a linked in the case of the reference image information P11 and the structure H, the output is linked to 90% risk and 30% risk, but the risk is extremely high compared to the previous case. Since this is a high case, the degree of association of w13 leading to 90% risk is set to 7 points, and the degree of association of w14 leading to 30% risk is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, it is not limited to the neural network, and may be composed of any decision-making factor that constitutes artificial intelligence.

In the example of the degree of association shown in FIG. 3, the node 61b is a node of a combination of the reference image information P11 and the structure F of the reference structure information, and the degree of association of 60% risk is w15 and the risk degree is 0. The degree of association of% is w16. The node 61c is a node that is a combination of the structure G (for example, the structure information regarding the arrangement of the garden) of the reference structure information and the structure I (for example, the structure information regarding the arrangement of the fence) with respect to the reference image information P12. , The degree of association of 30% risk is w17, and the degree of association of 70% risk is w18.

Such a degree of association is the learned data in artificial intelligence. After creating such learned data, when actually determining a new risk from now on, the risk is determined using the learned data described above. In such a case, the image information is newly acquired and the structure information is acquired. The image information corresponds to the reference image information, and the structure information corresponds to the reference structure information.

The image information to be newly acquired is captured by the camera of the information acquisition unit 9 described above. This photographing is the same as the image outside the building structure photographed for obtaining the above-mentioned reference image information. Further, the shooting conditions are not necessarily required to the same shooting conditions (shooting angle, angle of view, resolution) for obtaining the reference image information described above.

To obtain the structural information, the database in which the structural information of the building structure whose risk level is to be determined is recorded is accessed. For example, when the structural information is recorded in the database in association with the address, the structural information linked to the address may be read by inputting the address.

Based on the image information newly acquired in this way and the approach information of the train, the degree of risk at the time when the new image information is actually acquired is obtained. In such a case, the pre-acquired association degree shown in FIG. 3 (Table 1) is referred to. For example, when the newly acquired image information is the same as or similar to P12 and the acquired structure information is structure I, the node 61d is associated via the association degree. The node 61d is associated with "risk level 60%" at w19 and "risk level 70%" at association level w20. In such a case, “the risk level of 60%” having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one having the highest degree of association as the optimal solution, and “the risk 70%” in which the degree of association is low but the degree of association is recognized may be selected as the optimal solution. In addition to this, it goes without saying that an output solution to which no arrow is connected may be selected, and any other priority may be selected as long as it is based on the degree of association.

In this way, the selection of these optimum solutions uses the learned model shown in FIG. 3, and when the input data (image information, structure information) for which the solution is actually desired is input, the output solution (risk level) is output. It can be realized through artificial intelligence. However, in the present invention, it is not essential to utilize artificial intelligence, and any combination of reference image information and reference structure information and the degree of risk of the combination may be used in three or more levels. It may be realized in any form.

Table 2 below shows examples of the association degrees w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the association degrees w1 to w12 extending from this input. That is, the greater the association degrees w1 to w12, the heavier the weighting in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all be the same value, and the weighting in the selection of the intermediate node 61 may be all the same.

FIG. 4 illustrates an example in which a combination of the above-described reference image information and reference location information and the degree of association with the risk for the combination is set in three or more stages.

As the input data, such reference image information and reference location information are arranged. A combination of the reference image information as the input data and the reference location information is the intermediate node shown in FIG.

The reference location information includes all information for identifying the location of the building structure. The reference location information includes not only the address information and the map information but also an image of the surrounding environment including the building structure. Also included in this reference location information is the status of the building next to the building structure and what purpose the building next to the building structure is used for, such as shops, buildings, restaurants, vacant lots, parking lots, schools, etc. .. In other words, if the adjacent land is a vacant land, it is likely that the vacant land will easily penetrate into the building structure, and this is also a factor that controls the degree of danger.

The discriminator 2 acquires in advance the degrees of association w13 to w22 of three or more levels shown in FIG. In other words, the discrimination device 2 accumulates the reference image information, the reference location information, and the data indicating the degree of danger in that case in analyzing the actual degree of danger, and analyzes them. , The association degree shown in FIG. 4 is created by analysis.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference location information is the location J with respect to the reference image information P11, the degree of association of 60% is w15, and the degree of risk is 0. The degree of association of% is w16.

Similarly, when such a degree of association is set, image information is newly acquired and location information is acquired. The image information corresponds to the reference image information, and the location information corresponds to the reference location information.

The location information is accessed by accessing a database in which the structural information of the building structure whose risk level is to be determined is recorded. For example, when the location information is recorded in the database in association with the address, the location information linked to the address (neighboring place is relatively bright in a restaurant, or neighboring place is relatively bright) is entered by entering the address. (For example, a suspicious person is likely to intrude at the parking lot).

In obtaining the degree of risk, reference is made to the degree of association acquired in advance and shown in FIG. For example, when the acquired image is the same as or similar to the reference image information P12 and the location information is the location M, the combination is associated with the node 61c, and the node 61c has a risk degree of 30% and the association degree w17. And the risk level 70% is associated with the association level w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

FIG. 5 shows an example in which a combination of the above-described reference image information and reference home pattern information and the degree of risk of the combination is set in three or more levels.

As input data, such reference image information and reference in-home pattern information are arranged. A combination of the reference image information as the input data and the reference location information is the intermediate node shown in FIG.

The in-home reference pattern information for reference includes all kinds of information for identifying the in-home pattern of a resident who lives in the building structure. The home pattern information for reference includes, for example, the amount of electric power used in a building structure and the images of the resident's entrance and exit taken by cameras installed at the entrances and gates, based on the time when the resident is at home and going out. It is a pattern of the time zone in which it is operating. From Monday to Sunday, the resident's at-home time zone and out-of-home time zone are patterned for each time zone on each day of the week based on the above-described power consumption and images. At this time, the above-described power consumption amount and image may be machine-learned in advance to determine the pattern.

The discriminating device 2 acquires in advance the association degrees w13 to w22 of three or more stages shown in FIG. That is, the discrimination device 2 stores reference image information, reference home patterns, and data indicating the degree of risk in that case in analyzing the actual risk, and analyzes them. By analyzing, the association degree shown in FIG. 5 is created.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference location information is the location J with respect to the reference image information P11, the degree of association of 60% is w15, and the degree of risk is 0. The degree of association of% is w16.

Similarly, when such a degree of association is set, image information is newly acquired and at-home pattern information is also acquired. The image information corresponds to the reference image information, and the at-home pattern information corresponds to the reference-at-home pattern information.

In order to acquire the in-home pattern information, the in-home pattern information of the building structure whose risk level is to be determined is accessed. For example, when the home-at-home pattern information is associated with an address and recorded in the database, the home-at-home pattern information linked to the address by entering the address (Monday-Friday, 9:00 to 21:00 is It is also possible to read out when the user is out and at home other than that, and on Saturdays and Sundays, when he is out from 10:00 to 15:00 and otherwise stays at home.

In obtaining the degree of risk, reference is made to the degree of association acquired in advance and shown in FIG. For example, when the acquired image is the same as or similar to the reference image information P12 and the in-home pattern information is the in-home pattern R, the combination is associated with the node 61c, and the node 61c is associated with the risk degree of 30%. The degree w17 is associated with the degree of risk 70%, and the risk degree 70% is associated with the degree of association w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

In FIG. 6, a combination of three or more levels of the combination of the above-described reference image information and the reference time zone information indicating the time zone at the time of shooting of the reference image information, and the risk degree for the combination is set. It shows an example.

As the input data, such reference image information and reference time zone information are arranged. A combination of the reference image information as such input data and the reference time zone information is the intermediate node shown in FIG.

The reference time zone information means the time zone at the time of shooting of the reference image information, and not only the time at the temporary point but also the time such as 10:10 to 10:20. It may have a width.

The discriminator 2 acquires in advance the degree of association w13 to w22 of three or more levels shown in FIG. That is, the discrimination device 2 stores reference image information, reference home patterns, and data indicating the degree of risk in that case in analyzing the actual risk, and analyzes them. By analyzing, the association degree shown in FIG. 6 is created.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference time zone information is the combination of the time zone S with respect to the reference image information P11. The degree of association of 0% is w16. In other words, there are times when crime is less likely to be intruded by suspicious persons and times when it is greater. Especially, when the number of suspicious persons intrudes in the daytime is small and the number of suspicious persons in the nighttime is large, the degree of risk is analyzed in advance in relation to the reference image information for each of the time zones S to V,... Are stored in association with each other.

Similarly, when such a degree of association is set, image information is newly acquired and at-home time zone information is acquired. The image information corresponds to the reference image information, and the time zone information corresponds to the reference at-home time zone information.

The time zone information is acquired by extracting the time zone at the time of capturing the image information of the building structure whose risk level is to be determined. Then, the extracted time zone information is compared with the reference time zone information to make a determination. In such a case, the database in which the reference time zone information is recorded is accessed. For example, when the time zone information is from 9:20 to 9:30 (time zone V), the degree of risk is determined via the time zone V (9:20 to 9:30) of the same reference time zone information. To seek.

In obtaining the risk level, the pre-acquired association level shown in FIG. 6 is referred to. For example, when the acquired image is the same as or similar to the reference image information P12 and the time zone information is the time zone V, the combination is associated with the node 61c, and the node 61c is associated with the risk degree of 30%. The degree w17 is associated with the degree of risk 70%, and the risk degree 70% is associated with the degree of association w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

FIG. 7 shows an example in which, in addition to the above-described reference image information and reference structure information, a combination of reference person information and a degree of risk for the combination are set in three or more levels. Showing.

The reference person information is information obtained by extracting the person shown in the reference image information. The person information includes facial features (face contour, conspicuousness, hairstyle, presence/absence of glasses, wrinkles, scratches, etc.), age and gender estimated from facial features, and the like. A neural network may be used as the method for extracting the facial features. In such a case, (1) edge extraction is performed from the image data of the facial portion. (2) A skin color region is extracted from the image data of the face portion, (3) A region corresponding to the skin color region extracted in (2) is extracted from the edge image generated in (1), (4) Corresponding to the skin color region A well-known method is used, such as calculating cumulative histograms in the vertical direction and the horizontal direction from the edge image, and (5) using the number of pixels in the cumulative histogram in which the brightness is equal to or greater than a predetermined threshold value as NN (neural network) input data. You may do so. Further, (6) NN may be used to estimate the age and sex of the visitor based on the input data and the teacher data.

In such a case, as shown in FIG. 7, the degree of association is expressed as a set of combinations of the reference image information, the reference structure information, and the reference person information as nodes 61a to 61e of the intermediate nodes as described above. Will be done.

For example, in FIG. 7, in the node 61c, the reference image information P12 has the association degree w3, the reference structure information “structure G” has the association degree w7, and the reference person information “person β (for example, the person information is For a wife who lives next door, who often visits,” is associated with a degree of association of w11. Similarly, in the node 61e, the reference image information P13 has the association degree w5, the reference structure information “structure G” has the association degree w8, and the reference person information “person α (for example, has never been so far). Males in their 40s" are associated with a degree of association of w10. Even when the reference person information is not registered, the fact that it is not registered may be treated as one piece of reference person information.

Similarly, when such a degree of association is set, the degree of risk at the time when the new image information is actually acquired is calculated based on the newly acquired image information, the structure information, and the person information. The personal information referred to here is extracted from the actually acquired image information by performing image analysis by the same method as the extraction of the reference personal information.

The degree of association shown in FIG. 7, which is acquired in advance, is referred to when the degree of risk is calculated. For example, when the acquired image is the same as or similar to the reference image information P12, the reference structure information is “structure G”, and the extracted person information is the person β, the combination is associated with the node 61c. The node 61c is associated with the risk degree 30% as the association degree w17 and the risk degree 70% as the association degree w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

If the person is not registered in the reference person information, it is assumed to be the person β, and even if the extracted person information is a person who is not registered in the reference person information, the person β is regarded as being input and the solution search is performed. Becomes

It should be noted that this reference person is also used for the combination of the reference image information and the reference location information, the combination of the reference image information and the reference home pattern information, and the combination of the reference image information and the reference time information. The degree of association may be defined by further including a combination with information. Further, the degree of association may be defined by a combination of the reference image information and the reference person information.

FIG. 8 shows an example in which, in addition to the above-described reference image information and reference structure information, a combination of reference audio information and the degree of risk of the combination are set in three or more levels. Showing.

The reference audio information is audio that is actually recorded through a microphone or the like at the time of shooting the reference image information (or a predetermined time before and after the shooting). The analysis of the voice information may be performed by well-known means or may be finely analyzed by using the NN. Further, the audio information may be analyzed on the frequency axis.

In such a case, as shown in FIG. 8, the degree of association is such that the set of combinations of the reference image information, the reference structure information, and the reference voice person information is the intermediate nodes 61a to 61e as described above. Will be expressed.

For example, in FIG. 8, in the node 61c, the reference image information P12 has the association degree w3, the reference structure information “structure G” has the association degree w7, and the reference voice information “speech X” has the association degree w11. They are linked.

Similarly, when such a degree of association is set, the degree of risk at the time when the new image information is actually acquired is calculated based on the newly acquired image information, the structure information, and the audio information. The audio information mentioned here is the audio actually recorded through a microphone or the like at the time of shooting the image information (or a predetermined time before and after the shooting). The analysis of the voice information may be performed by well-known means or may be finely analyzed by using the NN. Further, the audio information may be analyzed on the frequency axis.

To obtain this risk, reference is made to the degree of association acquired in advance and shown in FIG. For example, when the acquired image is the same as or similar to the reference image information P12, the reference structure information is “structure G”, and the extracted voice information is the voice X, the combination is associated with the node 61c. The node 61c is associated with the risk degree 30% as the association degree w17 and the risk degree 70% as the association degree w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

It should be noted that this reference voice is also applied to the combination of the reference image information and the reference location information, the combination of the reference image information and the reference home pattern information, and the combination of the reference image information and the reference time information. The degree of association may be defined by further including a combination with information. Further, the degree of association may be defined by a combination of the reference image information and the reference audio information.

FIG. 8 shows an example in which, in addition to the above-described reference image information and reference structure information, a combination of reference audio information and the degree of risk of the combination are set in three or more levels. Showing.

The reference audio information is audio that is actually recorded through a microphone or the like at the time of shooting the reference image information (or a predetermined time before and after the shooting). The analysis of the voice information may be performed by well-known means or may be finely analyzed by using the NN. Alternatively, the audio information may be analyzed on the frequency axis.

In such a case, as shown in FIG. 8, the degree of association is such that the set of combinations of the reference image information, the reference structure information, and the reference voice person information is the intermediate nodes 61a to 61e as described above. Will be expressed.

For example, in FIG. 8, in the node 61c, the reference image information P12 has the association degree w3, the reference structure information “structure G” has the association degree w7, and the reference voice information “speech X” has the association degree w11. They are linked.

Similarly, when such a degree of association is set, the degree of risk at the time when the new image information is actually acquired is calculated based on the newly acquired image information, the structure information, and the audio information. The audio information mentioned here is the audio actually recorded through a microphone or the like at the time of shooting the image information (or a predetermined time before and after the shooting). The analysis of the voice information may be performed by well-known means or may be finely analyzed by using the NN. Further, the audio information may be analyzed on the frequency axis.

To obtain this risk, reference is made to the degree of association acquired in advance and shown in FIG. For example, when the acquired image is the same as or similar to the reference image information P12, the reference structure information is “structure G”, and the extracted voice information is the voice X, the combination is associated with the node 61c. The node 61c is associated with the risk degree 30% as the association degree w17 and the risk degree 70% as the association degree w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

It should be noted that this reference voice is also applied to the combination of the reference image information and the reference location information, the combination of the reference image information and the reference home pattern information, and the combination of the reference image information and the reference time information. The degree of association may be defined by further including a combination with information. Further, the degree of association may be defined by a combination of the reference image information and the reference audio information.

FIG. 9 shows an example in which, in addition to the above-described reference image information and reference structure information, a combination of reference security service information and the degree of risk of the combination is set in three or more levels. Is shown.

The reference security service information is whether or not the building structure has a contract with the security service of the security company, or the contract status regarding the specific contract content. The contract is, for example, when a door is broken or a window glass is broken, a security company comes to patrol a building structure from a security company.

In such a case, as shown in FIG. 8, the degree of association is such that the set of the combination of the reference image information, the reference structure information, and the reference security service information is the intermediate nodes 61a to 61e as described above. Will be expressed.

For example, in FIG. 8, in the node 61c, the reference image information P12 has the association degree w3, the reference structure information “structure G” has the association degree w7, and the “uncontracted” as the reference security service information has the association degree w11. Are associated with each other.

Even when such a degree of association is set, similarly, based on the newly acquired image information, the structure information, and the security service information, the degree of risk at the time when the new image information is actually acquired is obtained. .. The security service information mentioned here relates to whether or not the building structure to be discriminated is actually contracted for security service of the security company, or the contract status regarding the specific contract content.

The degree of association shown in FIG. 9 that is acquired in advance is referred to when the degree of risk is calculated. For example, when the acquired image is the same as or similar to the reference image information P12, the reference structure information is “structure G”, and the extracted security service information is “uncontracted”, the combination is associated with the node 61c. In this node 61c, the risk level of 30% is associated with the association level w17, and the risk level of 70% is associated with the association level w18. As a result of such a degree of association, the degree of risk at the time when the new image information and approach information are actually acquired is obtained based on w17 and w18.

It should be noted that this reference security is also applied to the combination of the reference image information and the reference location information, the combination of the reference image information and the reference home pattern information, and the combination of the reference image information and the reference time information. The degree of association may be defined by further including a combination with service information. Further, the association degree may be defined by a combination of the reference image information and the reference security service information.
The example of FIG. 10 is an example of using the degree of association of the reference image information and the risk degree in three or more stages.

It is assumed that the reference image information and the degree of risk are linked to each other to form a degree of association. In the example of FIG. 10, it is assumed that the input data is, for example, reference image information P11 to P13. The reference image information as such input data is connected to the output. In this output, the degree of danger as an output solution is assumed.

The reference image information is associated with each other through the association degree of three or more levels with respect to the risk degree as the output solution. The reference image information is arranged on the left side via the degree of association, and the respective risk degrees are arranged on the right side via the degree of association.

The discriminating device 2 acquires in advance the association degrees w13 to w19 of three or more stages shown in FIG. That is, in determining the actual search solution, the determination device 2 accumulates past data to determine what degree of risk the reference image information obtained by imaging in the past has accumulated, and analyzes these. The degree of association shown in FIG. 10 is created by analysis.

For example, assume that the reference image information is P11. It is assumed that the risk of P11 is 90%. By collecting and analyzing such data sets, the degree of association between the reference image information P11 and the degree of risk becomes strong.

This analysis may be performed by artificial intelligence. Also, deep learning may be used for image discrimination. Further, the degree of association shown in FIG. 10 may be configured by the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, it is not limited to the neural network, and may be composed of any decision-making factor that constitutes artificial intelligence.

In the process of constructing the learned model based on the degree of association, reference structure information including structure information about the building structure is also acquired. This reference structure information is not included in the above-mentioned degree of association.

Such a degree of association is the learned data in artificial intelligence. After creating such learned data, the solution is searched. In such a case, the captured image information is acquired and the structure information is also acquired in the same manner.

First, the risk degree is searched based on the newly acquired image information. In such a case, the degree of association acquired in advance and shown in FIG. 10 is used. For example, when the newly acquired image information is the same as or similar to the reference image information P12, the risk degree of 30% is the association degree w15 and the risk degree of 60% is the association degree via the association degree. It is associated with w16. In such a case, the risk with the highest degree of association, 30%, is selected as the optimum solution. However, it is not essential to select the one having the highest degree of association as the optimum solution, and the degree of association 60% at which the degree of association is low but the degree of association is recognized may be selected as the solution. In addition to this, it goes without saying that an output solution to which no arrow is connected may be selected, and any other priority may be selected as long as it is based on the degree of association. The output solution to be selected is not limited to one, and two or more may be selected. In such a case, two or more may be selected in order from the highest degree of association, but the present invention is not limited to this and may be based on any other priority of association.

The degree of risk obtained through the degree of association may be further modified based on the structural information, or the weighting may be changed.

For example, the structure F of the reference structure information is a single house, is an open exterior structure, and a fence is not built around the structure, and the structure G of the reference structure information is surrounded by a fence having a height of 2 m. It is assumed to be a structure. At this time, in the case of a structure having a low risk level such as the structure G of the reference structure information, it is set in advance to perform the process of reducing the weight of the risk level, in other words, the process of reducing the risk level itself. On the other hand, in the case of a structure having a high degree of risk, such as the structure F of the reference structure information, it is preset to perform the process of increasing the weight of the risk, in other words, the process of increasing the risk itself. deep.

In this way, after the setting with the reference structure information, when the actually acquired structure information is the same as or similar to the structure G of the reference structure information, the risk weighting process is performed to reduce the risk. If this is done, processing is performed to reduce the risk itself. On the other hand, when the actually acquired structure information is the same as or similar to the structure F of the reference structure information, the process of increasing the weight of the risk is performed, in other words, the process of increasing the risk itself.

In such a case, as a result of acquiring the structure information, when the safety is high as in the case where the actually acquired structure information is the same as or similar to the structure G, for example, as a result of performing the process of lowering the weight of the risk, The risk itself may be determined as 0% without making the determination itself using the degree of association described above.

The same applies when the reference location information is used as an alternative to the reference structure information.

Further, although FIG. 11 shows an example in which the reference location information is used as an alternative to the reference structure information, similar processing operations are executed in such a case. According to the location information for reference, depending on the location of the building structure, the weighting of the risk is changed depending on whether or not the safety is high.

Further, in the case shown in FIG. 10, it is needless to say that the reference home pattern information, the reference time zone information, and the reference security service information may be applied instead of the reference structure information.

Further, FIG. 12 is an example of a case where the reference traffic information is used as an alternative to the reference structure information. The reference traffic volume information is information regarding the traffic volume of vehicles or passersby on the road around the building structure. This traffic volume is the number of vehicles or passersby passing per unit time. It has already been reported that a building structure facing a road with more traffic tends to be less likely to be intruded by a suspicious person, so this is included in the explanatory variable to determine the degree of risk. ..

For example, it is assumed that the reference traffic volume information F has a traffic volume of 100 persons per unit time, and the reference traffic volume information G has one person per unit time. At this time, when the degree of risk is low as in the reference traffic amount information F, the process of lowering the weight of the risk is performed, in other words, the process of lowering the risk itself is set in advance. On the other hand, when the degree of risk is high as in the reference traffic amount information G, the process of increasing the weight of the risk is performed, in other words, the process of increasing the risk itself is set in advance.

After the setting with the reference traffic amount information, when the actually acquired traffic amount information is the same as or similar to the reference traffic amount information G, the process of increasing the weighting of the risk is performed, In other words, the process of increasing the risk itself is performed. On the other hand, when the actually obtained traffic volume information is the same as or similar to the reference traffic volume information F, the process of lowering the weighting of the risk is performed, in other words, the process of lowering the risk itself is performed.

As a result of acquiring the traffic volume information in such a case, when the traffic volume information actually acquired is the same as or similar to the reference traffic volume information F, when the safety is high, for example, weighting of the degree of risk is performed. As a result of performing the lowering process, the risk itself may be determined to be 0% without performing the above-described determination itself using the degree of association.

For the above-mentioned traffic information and traffic information for reference, the data of traffic volume surveys conducted by municipalities, countries, and other organizations may be used as they are, or the building structure imaged in a unit time may be used. The determination may be made based on the image on the road. In such a case, the inspector may count the number of vehicles and passers-by in the image one by one, or the well-known deep learning technology may be used to extract and identify the vehicles and passers-by, The number of specified vehicles or passers-by per unit time may be counted.

Further, FIG. 13 is an example of a case where the reference crime history information is used as an alternative to the reference structure information. The reference crime history information is information about past crime history in the area of the building structure. The criminal history includes all kinds of crimes such as murder, burglary, burglary, kidnapping, robbery, and sex crimes. The crime history may be quantified by the number of crimes per unit period, or the numerical value may be weighted according to the weight of the crime. Make adjustments so that the higher the crime history, the higher the risk.

The crime history of each area may be made available by using information published on the Internet, or may be extracted from an article or the like reported on TV or newspapers and input.

For example, it is assumed that the reference crime history information F contains only one burglar in the area in the past 10 years. At this time, if the risk level is low as in the reference crime history information F, the process of lowering the weighting of the risk level, in other words, the process of lowering the risk level itself is set in advance. On the other hand, when the reference crime history information G has a high degree of risk, such as when 10 burglars have been entered in the past 10 years, a process of increasing the weight of the risk is performed, in other words, the risk itself is set. It is set in advance to perform the raising process.

After the setting with the reference crime history information in this way, when the actually obtained crime history information is the same as or similar to the reference crime history information G, the process of increasing the weight of the risk is performed, In other words, the process of increasing the risk itself is performed. On the other hand, when the crime history information actually acquired is the same as or similar to the reference crime history information F, the risk weighting is reduced, in other words, the risk itself is reduced.

In such a case, as a result of the crime history information being acquired, if the crime history information actually acquired is the same as or similar to the reference crime history information F, if the safety is high, for example, the degree of risk is weighted. As a result of performing the lowering process, the risk itself may be determined to be 0% without performing the determination itself using the degree of association described above.

Further, as shown in FIGS. 10 to 13, the degree of association is not limited to the case of being formed in the relationship between the reference image information and the risk, and as shown in FIG. Alternatively, it is possible to acquire in advance three or more levels of association between the combination of the reference image information and the reference audio information recorded at the time of shooting and the degree of risk for the combination. On the other hand, the reference information linked to any of the reference structure information, the reference location information, the reference home pattern information, the reference time zone information, and the reference security service information is referred to, and the suspicious person's intrusion is prevented. The risk level may be determined.

In the example of FIG. 15, an example is shown in which the degree of risk and the degree of association are formed with the reference person information extracted from the reference image information. The same processing operation is executed also when forming the degree of association in relation to the risk degree for this reference person information. In other words, by analyzing the reference image information, the degree of association of three or more levels with the risk degree for the reference person information in which the person is extracted is acquired in advance. Next, when newly determining the degree of risk, the person information extracted from the person is further obtained by analyzing the image information obtained by photographing the outside of the building structure. The degree of risk is determined through the reference person information corresponding to the acquired person information.

At this time, as a substitute for the reference person information, the same processing operation is executed also in the case of forming the degree of association in the relationship between the risk information and the action information typifying a series of movements and poses of the person shown in the image. .. In other words, by analyzing the reference image information, the degree of association of three or more levels with the risk degree with respect to the reference action information in which the action of the person is extracted is acquired in advance. Next, when newly determining the degree of risk, the action information in which the action of the person is extracted is further obtained by analyzing the image information obtained by photographing the outside of the building structure. The risk level is determined through the reference action information corresponding to the obtained action information.

A so-called posture estimation technique may be used for the reference action information and the action information. The posture estimation technique may use deep learning. For example, the feature points of the nose, eyes, ears, neck, shoulders, elbows, wrists, waist, knees, and ankles are detected by deep learning. Then, the posture is estimated based on the dynamic changes of these feature points. As free software for posture estimation, for example, OpenPose (registered trademark) developed by Carnegie Mellon University may be used.

In the present invention, as shown in FIG. 16, reference image information is input as input data, a risk level is output as output data, and at least one hidden layer is provided between the input node and the output node. Alternatively, machine learning may be performed. The above-mentioned degree of association is set in either or both of the input node and the hidden layer node, and this becomes the weighting of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold, the output may be selected.

According to the present invention having the above-described configuration, it is possible to easily determine the degree of intrusion risk of a suspicious person in a building structure with a small amount of labor without requiring special skill. Further, according to the present invention, it is possible to make the determination of the degree of risk with higher accuracy than a human. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Further, the present invention is characterized in that the optimum physical properties and the generation mechanism are searched for through the association degrees set in three or more stages. The degree of association can be described by a numerical value from 0 to 100%, for example, in addition to the above-mentioned five levels, but is not limited to this, and at any level as long as it can be described by a numerical value of three or more levels. It may be configured.

By searching for the most probable risk based on the degree of association represented by three or more levels, the degree of association is high in situations where there are multiple possible candidates for higher risk. It is also possible to search and display in order. If it is possible to display to the user in the descending order of the degree of association, it is possible to preferentially display the more probable risk, and it is possible to call attention to the higher risk.

When the degree of risk is high, it is possible to avoid the danger by contacting a security service company or the police, or by promptly calling a resident of the building structure a caution. Especially when the degree of risk is higher, it is possible to protect the safety by alerting residents through voices or the like or by generating a voice outside to discourage a suspicious person from intruding. Further, according to the present invention, since the accuracy of detecting the degree of risk is high, it is only necessary to carry out the operation of calling the police only when it is really necessary without calling the police unnecessarily.

In addition to this, according to the present invention, it is possible to make a determination without missing the determination result of an output having an extremely low degree of association such as 1%. Remind users that even output discrimination results with a very low degree of association are connected as slight signs, and may be useful as the discrimination results once every tens or hundreds of times. can do.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is set low, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but there is a low possibility that a more appropriate discrimination result can be suitably detected, and a lot of noise may be picked up. is there. On the other hand, if the threshold value is set high, it is highly possible that the optimal risk can be detected with high probability, but normally, the degree of association is low, and although it is passed through, it appears once every tens or hundreds of times. In some cases, the solution may be overlooked. It is possible to decide which is to be emphasized based on the idea of the user side and the system side, but it is possible to increase the degree of freedom in selecting such a point to be emphasized.

Further, according to the present invention, as shown in FIG. 17, the determination is made based on the degree of association of a combination of two or more types of information, that is, reference information U and reference information V. The reference information U, V is each reference information including the above-mentioned reference image information.

At this time, as shown in FIG. 17, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, with respect to the reference information U (reference image information), after outputting an output solution as shown in FIG. 3, this is input as it is and the degree of association with other reference information V is used, The output may be searched.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition, input parameters based on the camera image captured in the building structure, the acquired reference image information, reference location information, reference home pattern information, reference time information, reference voice information, reference security service information, etc. When new knowledge is found regarding the relationship between the output solution and the output solution (risk level), the degree of association is increased or decreased according to the knowledge.

In other words, this update is equivalent to learning in artificial intelligence. This is a learning act because new data is acquired and reflected in the learned data.

Further, not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like may be used for the process of first creating the learned model and the above-described update. In the case of unsupervised learning, instead of reading a data set of input data and output data for learning, information corresponding to the input data (reference image information, reference location information, reference home pattern information, reference (Time information, reference voice information, reference security service information, etc.) may be read and learned, and the degree of association related to output data may be self-formed therefrom.

This update of the degree of association is not based on the information that can be acquired from the public communication network, but is also artificial on the system side or the user side based on the contents of research data and papers by academic experts, academic conference presentations, newspaper articles, books, etc. Alternatively, it may be automatically updated. You may make it utilize artificial intelligence in these update processes.

In addition, in the risk level determination system 1 to which the present invention is applied, when the risk level is high, an alarm is generated or "unsolicited" in addition to reporting to the residents of the building structure, the management company, and the security company Of course, a voice such as "Please do not enter" may be generated.

1 Danger Level Discrimination System 2 Discrimination Device 21 Internal Bus 23 Display Unit 24 Control Unit 25 Operating Unit 26 Communication Unit 27 Estimating Unit 28 Storage Unit 61 Node

Claims (10)

In the risk determination program for determining the risk of suspicious person intrusion into the building structure,
A pre-acquisition degree of at least three levels with respect to the risk with respect to the reference image information obtained by photographing the outside of the building structure, and an association degree acquisition step of acquiring the reference structure information including the structure information regarding the building structure; ,
When newly determining the degree of risk, an information acquisition step of acquiring image information by newly photographing the outside of the building structure and acquiring structure information of the building structure,
Utilizing the degree of association acquired in the degree of association acquisition step, referring to the reference structure information linked to the structure information, and based on the image information obtained through the information acquisition step, the suspicion of the building structure A risk level determination program characterized by causing a computer to execute a determination step for determining a risk level of intruder. In the risk determination program for determining the risk of suspicious person intrusion into the building structure,
The degree of association with the risk of the reference image information obtained by photographing the outside of the building structure is acquired in advance, and the degree of association is acquired to obtain the reference location information for specifying the location of the building structure. Steps,
An information acquisition step of acquiring image information by newly photographing the outside of the building structure when newly determining the degree of risk, and acquiring location information indicating the location of the building structure,
Together utilizing association degree acquired in the association degree obtaining step, with reference to the reference location information to link to the location information, based on the reference location information to link the acquired location information via the information acquisition step, A risk level determination program, characterized by causing a computer to execute a determination step for determining a risk level of a suspicious person entering the building structure. In the risk determination program for determining the risk of suspicious person intrusion into the building structure,
An association that acquires in advance three or more levels of association with the risk level for reference image information of the outside of the building structure and also obtains home reference pattern information indicating the home pattern of the resident of the building structure. Degree acquisition step,
An information acquisition step of acquiring image information by newly photographing the outside of the building structure when determining the degree of risk, and acquiring at-home pattern information indicating the at-home pattern of the building structure,
While utilizing the degree of association obtained in the degree of association obtaining step, referring to the reference home pattern information linked to the home pattern information, based on the image information obtained through the information obtaining step, to the building structure A risk determination program that causes a computer to execute a determination step for determining the risk of intrusion of a suspicious person. In the risk determination program for determining the risk of suspicious person intrusion into the building structure,
In addition to acquiring in advance three or more levels of association with the degree of risk with respect to the reference image information of the outside of the building structure, the reference time zone information indicating the time zone at the time of capturing the reference image information is acquired. The step of obtaining the degree of association,
An information acquisition step of acquiring image information by newly capturing an image of the outside of the building structure when newly determining the degree of risk, and acquiring time zone information in the time zone at the time of the capturing,
While utilizing the degree of association obtained in the degree of association obtaining step, referring to the reference time zone information linked to the time zone information, based on the image information obtained through the information obtaining step, to the building structure A risk determination program that causes a computer to execute a determination step for determining the risk of intrusion of a suspicious person. In the risk determination program for determining the risk of suspicious person intrusion into the building structure,
An association that acquires in advance three or more levels of association with the degree of risk for reference image information obtained by photographing the outside of the building structure, and also obtains reference security service information indicating the contract status of the security service of the building structure. Degree acquisition step,
When newly determining the degree of risk, an information acquisition step of acquiring image information by newly photographing the outside of the building structure and acquiring security service information indicating the contract status of the security service in the building structure When,
While utilizing the degree of association obtained in the degree of association obtaining step, referring to the reference security service information linked to the security service information, based on the image information obtained through the information obtaining step, to the building structure. A risk determination program that causes a computer to execute a determination step for determining the risk of intrusion of a suspicious person. In the risk judgment program for judging the risk of suspicious person invading the building structure,
In addition to acquiring in advance three or more levels of association with the risk for the reference image information of the exterior of the building structure, the reference traffic information indicating the traffic of the roads around the building structure is acquired. The step of obtaining the degree of association,
When newly determining the degree of danger, image information is acquired by newly photographing the outside of the building structure, and information acquisition is also acquired that acquires traffic volume information that indicates the traffic volume of roads around the building structure. Steps,
While using the degree of association acquired in the degree of association acquisition step, referring to the reference traffic information linked to the traffic information, based on the image information acquired through the information acquisition step, to the building structure A risk determination program that causes a computer to execute a determination step for determining the risk of intrusion of a suspicious person. In the risk determination program for determining the risk of suspicious person intrusion into the building structure,
An association that acquires in advance three or more levels of association with the risk level for reference image information of the outside of a building structure and also obtains reference crime history information regarding past crime history in the area of the building structure. Degree acquisition step,
When newly determining the degree of risk, image information is acquired by newly photographing the outside of the building structure, and information is acquired to acquire crime history information indicating the past crime history in the area of the building structure. Steps,
While utilizing the degree of association obtained in the step of obtaining the degree of association, referring to the reference crime history information linked to the crime history information, based on the image information obtained through the information obtaining step, to the building structure A risk determination program that causes a computer to execute a determination step for determining the risk of intrusion of a suspicious person. In the association degree acquisition step, the association degree of three or more levels with respect to the reference person information in which the person is extracted by further analyzing the reference image information is obtained in advance,
In the information acquisition step, when newly determining the degree of risk, the person information extracted from the person by further analyzing the image information acquired by photographing the outside of the building structure is further acquired,
The risk determination program according to any one of claims 1 to 7, wherein in the determination step, the risk is determined based on the personal information acquired through the information acquisition step. In the association degree acquisition step, the association degree of three or more levels of the combination of the reference image information and the reference voice information recorded at the time of shooting the reference image information and the risk degree for the combination is acquired in advance. ,
In the information acquisition step, when newly determining the degree of risk, the outside of the building structure is photographed and the recorded voice information is further acquired,
Above determination step further based on the acquired speech information via the information acquisition step, according to claim 1 to 7 any one risk determination program according among, characterized in that to determine the degree of risk. In the risk determination system for determining the risk of suspicious person intrusion into a building structure,
And a degree-of-association acquisition unit that acquires in advance three or more levels of association with the risk with respect to the reference image information obtained by photographing the outside of the building structure, and also obtains the reference structure information including the structure information regarding the building structure. ,
When newly determining the degree of risk, while acquiring image information by newly photographing the outside of the building structure, information acquisition means for acquiring the structure information of the building structure,
While utilizing the degree of association obtained by the degree-of-association obtaining unit, referring to the reference structure information linked to the structure information, based on the image information obtained through the information obtaining unit, to the building structure A risk determination system comprising: a determination unit that determines the risk of intrusion of a suspicious individual.

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