JP6739115B6 - Risk determination program and system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To detect a risk level in advance and prevent an incident from occurring.
SOLUTION: In a risk level determination program for determining the risk level of a suspicious person entering a house, reference image information obtained by photographing the outside of the house and reference location information for specifying the location of the house are provided. A combination degree acquiring step of acquiring in advance three or more levels of degree of association between a combination that has and a degree of risk for the combination, and when newly determining the degree of risk, by newly photographing the outside of the house, image information can be obtained. While acquiring, referring to the information acquisition step of acquiring location information indicating the location of the house, and the degree of association acquired in the degree of association acquisition step, the image information and location information obtained through the information acquisition step Based on the above, the computer is caused to execute a determination step of determining the risk level of the suspicious person entering the house.
[Selection diagram] Fig. 4

Description

The present invention relates to a risk determination program and system for determining the risk of suspicious person invasion into a house.

Many attempts have been made to prevent suspicious persons from entering a house. In particular, single-family homes have more intrusion routes than condominiums, so it is necessary to take stronger preventive measures against suspicious intrusions in order to protect the safety of residents.

Japanese Patent Application No. 2016-210848

The above-mentioned disclosure technique of Patent Document 1 describes a technique of identifying a person from a face image by artificial intelligence, and quantitatively determines the risk of suspicious person invasion into an actual house by utilizing artificial intelligence. It's not something you can do. By actually predicting the degree of risk with high accuracy, if the resident is in danger, contact the security company or police as soon as possible, and if the degree of danger is not so high, It was necessary not to unnecessarily invite security guards and police officers from security companies. Such a viewpoint is not particularly described in the disclosure technique of Cited Document 1.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose of the present invention is to detect the risk of suspicious person invasion into a house in advance and to protect the safety of residents. The purpose of the present invention is to provide a risk level determination program and a system for automatically determining the risk level using artificial intelligence.

The risk determination program according to the present invention is a risk determination program for determining the risk of a suspicious person invading a house, and is for identifying reference image information obtained by photographing the outside of the house and the location of the house. A combination with reference location information and a degree of association acquisition step of acquiring three or more levels of association with the degree of risk for the combination in advance, and a new photograph of the outside of the house when determining the degree of danger. By doing so, the image information is acquired, and the information acquisition step for acquiring the location information indicating the location of the house and the association degree acquired in the association degree acquisition step are referred to, and the image acquired through the information acquisition step. Based on the information and the location information, the computer is made to perform a determination step of determining the risk of invasion of a suspicious person into the house.

The risk determination system according to the present invention is a risk determination system for determining the risk of suspicious person invasion into a house, for identifying reference image information obtained by photographing the outside of the house and the location of the house. An association degree acquisition means for acquiring in advance three or more levels of association between a combination having reference location information and a risk degree for the combination, and a new photograph of the outside of the house when determining the risk degree. The image information is acquired by the method, and the information acquisition means for acquiring the location information indicating the location of the house and the association degree acquired by the association degree acquisition means are referred to and acquired through the information acquisition means. It is characterized by providing a determination means for determining the risk of suspicious person invasion into the house based on the image information and the location information.

In order to detect the risk of suspicious person invasion into a house in advance and prevent an incident, the risk can be determined automatically and with high accuracy using artificial intelligence.

It is a block diagram which shows the whole structure of the risk degree determination system which implements the risk degree determination program to which this invention is applied. It is a figure which shows the specific configuration example of the discrimination apparatus. It is a figure for demonstrating operation in the risk determination program to which this invention is applied. It is a figure which shows the example which set the degree of association of 3 or more levels including the location information for reference. It is a figure which shows the example which set the degree of association of 3 or more levels including the home pattern information for reference. It is a figure which shows the example which set the degree of association of 3 or more steps including the reference time zone information. It is a figure which shows the example which set the degree of association of 3 or more levels including the person information for reference. It is a figure which shows the example which set the degree of association of 3 or more steps including the voice information for reference. It is a figure which shows the example which set the degree of association of 3 or more levels including the security service information for reference.

Hereinafter, the risk 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 determination system 1 in which a risk determination program to which the present invention is applied is implemented. The risk level discrimination system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of 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 configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2.

Database 3 stores information on the risk of suspicious person invasion into the house, such as an incident that occurred before in the house or a case that did not reach the case but had a high risk. The database 3 includes reference image information previously captured by a camera that actually constitutes the information acquisition unit 9, reference housing structure information including structural information related to the house, reference location information for specifying the location of the house, and the relevant information. Reference home pattern information indicating the home pattern of a resident of a house, reference time zone information indicating the time zone at the time of shooting the reference image information, and a reference person extracted by analyzing the reference image information. Information, reference audio 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 security company in the house, etc. are accumulated.

The discrimination 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 terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can determine the degree of danger in the invasion of a suspicious person into the house by obtaining the search solution by the determination device 2.

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

The control unit 24 is a so-called central control unit for controlling each component mounted in the discrimination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the 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 from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the determination unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The judgment unit 27 is responsible for various judgments regarding the risk of a suspicious person invading the house. The determination unit 27 reads out 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 well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by 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 composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read by the control unit 24 and executed.

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

In the risk level determination system 1, it is premised that a combination of reference image information and reference housing structure information is formed, for example, as shown in FIG. The reference image information is an image taken of the outside of the house by a camera installed in a house, a gate of the house, an entrance, a garden, an exterior, a fence, a fence, a parking space, a passageway, etc. Images of people passing by on the road outside the house, images of residents of the house entering the house through the gate, images of suspicious persons climbing the fence of the house and trying to invade, etc. Is.

The reference housing structure information is data indicating the structure of the house, for example, a photograph of the entire house taken from the air, a photograph of the house taken from the road side, electronic data of a completed drawing of the house, and a house shown on a map. It is electronic data such as an overall structural drawing.

In the example of FIG. 3, for example, the reference image information is the reference image information P11 to P13 taken at each place or each time series outside the house, and the structures F to I are used as the reference house structure information. Etc.

As the input data, such reference image information and reference housing structure information are arranged side by side. The intermediate node shown in FIG. 3 is a combination of the reference image information and the reference housing structure information as such input data. Each intermediate node is further linked to the output. In this output, the risk of suspicious person intrusion into the house as an output solution is displayed as a percentage.

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

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual risk level, the discrimination device 2 accumulates reference image information, reference approach information, and data on the degree of risk in that case, and analyzes these. , The degree of association shown in FIG. 3 is created by analysis.

For example, it is assumed that the reference image information P11 is an image of a resident of a house entering the house through a gate. In addition, when the structure F of the house to be discriminated is assumed, whether or not an incident that caused damage to the resident of the house occurred in the previous data, and even if the incident did not occur, an incident occurred. It is extracted whether or not the risk level was not strange, or whether or not it was particularly safe. These data can be obtained from the records of image data taken by past cameras for reference image information, and the reference structure information can be obtained from completed drawings and map data recorded by house makers and government offices. Alternatively, it may be extracted from an aerial image or image data of a house previously captured from a road. The risk level may be quantified by visually recognizing the reference image information by a plurality of people and collecting questionnaire surveys and the like regarding the risk level.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P11 and structure F, past data such as whether or not an incident actually occurred, or whether or not an incident did not occur but the risk was high, etc. Analyze from. The higher the risk of an incident or the higher the risk of an incident, the higher the degree of association that leads to a higher risk output, and the fewer cases of an incident, the higher the degree of association that leads to a lower risk output. 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 the output of 90% risk and 30% risk, but the risk is extremely high from the previous case. Since this is a high case, the degree of association of w13 that leads to a risk of 90% is set to 7 points, and the degree of association of w14 that leads to a degree of risk of 30% is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of 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, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

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

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

The newly acquired image information is captured by the camera by the information acquisition unit 9 described above. This shooting is the same as the image of the outside of the house taken in order to obtain the above-mentioned reference image information. Further, the shooting conditions are not required to the extent that the shooting conditions (shooting angle, angle of view, resolution) for obtaining the above-mentioned reference image information are all the same.

To obtain the housing structure information, access the database in which the housing structure information of the house for which the risk level is to be determined is recorded. For example, when the housing structure information is associated through the address and recorded in the database, the housing structure information linked to the address may be read out 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 newly acquired image information is actually acquired is obtained. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P12, and the acquired housing structure information is structure I, the node 61d is associated via the degree of association. The node 61d is associated with a "risk level of 60%" by w19 and a "risk level of 70%" by a degree of association w20. In such a case, the “risk degree 60%” having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and it is also possible to select “70% risk” as the optimum solution, which has a low degree of association but the association itself is recognized. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.
In this way, the selection of these optimum solutions uses the trained model shown in FIG. 3, and when the input data (image information, housing 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, the present invention does not necessarily utilize artificial intelligence, and utilizes a combination of reference image information, reference housing structure information, and a degree of association of three or more levels of risk with respect to the combination. If there is, it may be realized in any form.

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

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

FIG. 4 shows an example in which the combination of the above-mentioned reference image information, the reference location information, and the degree of risk for the combination are set to three or more levels of association.

As the input data, such reference image information and reference location information are arranged side by side. The intermediate node shown in FIG. 4 is a combination of reference image information and reference location information as such input data.
Reference location information includes any information that identifies the location of the home. The reference location information includes not only address information and map information, but also images of the surrounding environment including the house. The location information for reference also includes the status of the building next to the house and what purpose the land next to the house is used for, such as shops, buildings, restaurants, vacant lots, parking lots, and schools. In other words, if the adjacent land is a vacant lot, it is possible that the vacant lot can easily invade the house, which is also a factor that controls the degree of risk.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual risk level, the discrimination device 2 accumulates reference image information, reference location information, and data on the degree of risk in that case, and analyzes these. , The degree of association 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 a combination of 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.

To obtain the location information, access the database in which the housing structure information of the house for which the risk level is to be determined is recorded. For example, if the location information is associated through an address and recorded in the database, by entering the address, the location information linked to that address (the neighboring land is relatively bright at a restaurant, or the neighboring land is stationed). A suspicious person may easily invade the parking lot, etc.).

In determining the degree of risk, the degree of association shown in FIG. 4 acquired in advance is referred to. 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 level of 30% and a degree of association 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 danger at the time when the newly acquired image information and the approach information are actually acquired is obtained based on w17 and w18.

FIG. 5 shows an example in which the combination of the above-mentioned reference image information, the reference home pattern information, and the degree of risk for the combination are set to three or more levels of association.

As the input data, such reference image information and reference home pattern information are arranged side by side. The intermediate node shown in FIG. 5 is a combination of reference image information and reference location information as such input data.

The reference home pattern information includes all information for identifying the home pattern of the resident who resides in the house. As reference home pattern information, for example, based on the amount of electricity used in the house and the image of the resident entering and exiting taken by the camera installed at the entrance or the gate, the resident goes out during the time when he / she is at home. It is a pattern of the time zone. From Monday to Sunday, for each time zone of each day of the week, the resident's home time zone and outing time zone are patterned based on the above-mentioned power consumption and images. At this time, the above-mentioned power consumption and images may be machine-learned in advance to determine the pattern.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates reference image information, a reference home pattern, and data on the degree of risk in that case, and analyzes them in determining the actual risk level. , The degree of association shown in FIG. 5 is created by analysis.

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 combination of 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 home pattern information is acquired. The image information corresponds to the reference image information, and the home pattern information corresponds to the reference home pattern information.

To acquire the home pattern information, access the database in which the home pattern information of the house for which the risk level is to be determined is recorded. For example, if the home pattern information is associated through an address and recorded in the database, the home pattern information linked to that address by entering the address (Monday to Friday is from 9:00 to 21:00). It is also possible to read out (when going out, other than that is at home, on Saturdays and Sundays, from 10:00 to 15:00, when going out and at home, etc.).

In determining the degree of risk, the degree of association shown in FIG. 5 acquired in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12 and the home pattern information is the home pattern R, the combination is associated with the node 61c, and the node 61c is associated with a risk level of 30%. A degree w17 and a 70% risk are associated with a degree w18. As a result of such a degree of association, the degree of danger at the time when the newly acquired image information and the approach information are actually acquired is obtained based on w17 and w18.

In FIG. 6, the combination of the above-mentioned reference image information, the reference time zone information indicating the time zone at the time of shooting of the reference image information, and the degree of association with the risk degree for the combination are set in three or more stages. An example is shown.

As the input data, such reference image information and reference time zone information are arranged side by side. The intermediate node shown in FIG. 6 is a combination of the reference image information and the reference time zone information as such input data.

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

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates reference image information, a reference home pattern, and data on the degree of risk in that case, and analyzes them in determining the actual risk level. , The degree of association shown in FIG. 6 is created by analysis.

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 a combination of the reference time zone S with respect to the reference image information P11, and the association degree of 60% risk is w15, which is dangerous. The degree of association of 0% is w16. In other words, there are times when crimes are less likely to be invaded by suspicious persons and times when crimes are more frequent. In particular, when the invasion of suspicious persons is small in the daytime and conversely large in the nighttime, the degree of risk is analyzed in advance in relation to the reference image information for each time zone S to V, ..., And the intermediate node 61 It is associated and memorized in the degree of association as.

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

To acquire the time zone information, the time zone at the time of shooting the image information in the house for which the degree of risk is to be determined is extracted. Then, the extracted time zone information is compared with the reference time zone information to make a determination. In such a case, access the database in which the reference time zone information is recorded. For example, when the time zone information is from 9:20 to 9:30 (time zone V), the risk level is passed through the time zone V (9:20 to 9:30) of the same reference time zone information. I will seek.

In determining the degree of risk, the degree of association shown in FIG. 6 acquired in advance 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 a risk level of 30%. A degree w17 and a 70% risk are associated with a degree w18. As a result of such a degree of association, the degree of danger at the time when the newly acquired image information and the approach information are actually acquired is obtained based on w17 and w18.

FIG. 7 shows an example in which, in addition to the above-mentioned reference image information and reference housing structure information, a combination of reference person information and a degree of risk to the combination are set to three or more levels of association. Is shown.

The reference person information is information extracted from the person displayed in the reference image information. The person information includes facial features (face contour, prominence, 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 a method for extracting facial features. In such a case, (1) edge extraction is performed from the image data of the facial part. (2) Extract the skin color area from the image data of the face part, (3) Extract the area corresponding to the skin color area extracted in (2) from the edge image generated in (1), (4) Correspond to the skin color area. Utilizing well-known methods such as calculating cumulative histograms in each of the vertical and horizontal directions from the edge image, and (5) using the number of pixels of the cumulative histogram whose brightness is equal to or higher than a predetermined threshold as input data of NN (neural network). You may try to do it. Further, (6) NN may estimate the age and gender 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 such that the set of combinations of the reference image information, the reference housing structure information, and the reference person information is set as the intermediate node nodes 61a to 61e as described above. It will be expressed.

For example, in FIG. 7, in the node 61c, the reference image information P12 has an association degree w3, the reference housing structure information “structure G” has an association degree w7, and the reference person information “person β (for example, person information)”. In the case of a wife who lives next door who often visits) ”is linked with a degree of linkage w11. Similarly, in the node 61e, the reference image information P13 has a degree of association w5, the reference housing structure information "structure G" has a degree of association w8, and the reference person information "person α (for example, what has been up to now)". No male in his 40s) ”is linked with a degree of association w10. Even if it is not registered in the reference person information, the fact that it is not registered may be treated as one reference person information.

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

In determining the degree of risk, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12, the reference housing structure information "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 a degree of risk of 30% with a degree of association w17 and a degree of risk of 70% with a degree of association w18. As a result of such a degree of association, the degree of danger at the time when the newly acquired image information and the 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 not registered in the reference person information, the solution search is performed assuming that the person β is input. It becomes.

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 are also referred to as the reference person. The degree of association may be defined by further including the combination with information. Further, the degree of association may be defined by the combination of the reference image information and the reference person information.

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

The reference audio information is audio actually recorded through a microphone or the like at the time of shooting (or a predetermined time before and after the shooting) of the reference image information. The analysis of the voice information may be performed in detail by a well-known means or by using NN. Further, the voice 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 housing structure information, and the reference voice person information is the intermediate node nodes 61a to 61e as described above. Will be expressed as.

For example, in FIG. 8, in the node 61c, the reference image information P12 has an association degree w3, the reference housing structure information “structure G” has an association degree w7, and the reference audio information “voice X” has an association degree w11. Is linked with.

Similarly, when such a degree of association is set, the degree of risk at the time when the newly acquired image information is actually acquired is obtained based on the newly acquired image information, the housing structure information, and the audio information. .. The audio information referred to here is 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 in detail by a well-known means or by using NN. Further, the voice information may be analyzed on the frequency axis.

In determining the degree of risk, the degree of association shown in FIG. 8 acquired in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12, the reference housing structure information "structure G", and the extracted audio information is audio X, the combination is associated with the node 61c. The node 61c is associated with a risk level of 30% by an association degree w17 and a risk degree of 70% by an association degree w18. As a result of such a degree of association, the degree of danger at the time when the newly acquired image information and the approach information are actually acquired is obtained based on w17 and w18.

It should be noted that 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 are also referred to as the reference voice. The degree of association may be defined by further including the combination with information. Further, the degree of association may be defined by the combination of the reference image information and the reference audio information.

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

The reference audio information is audio actually recorded through a microphone or the like at the time of shooting (or a predetermined time before and after the shooting) of the reference image information. The analysis of the voice information may be performed in detail by a well-known means or by using NN. Further, the voice 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 housing structure information, and the reference voice person information is the intermediate node nodes 61a to 61e as described above. Will be expressed as.

For example, in FIG. 8, in the node 61c, the reference image information P12 has an association degree w3, the reference housing structure information “structure G” has an association degree w7, and the reference audio information “voice X” has an association degree w11. Is linked with.

Similarly, when such a degree of association is set, the degree of risk at the time when the newly acquired image information is actually acquired is obtained based on the newly acquired image information, the housing structure information, and the audio information. .. The audio information referred to here is 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 in detail by a well-known means or by using NN. Further, the voice information may be analyzed on the frequency axis.

In determining the degree of risk, the degree of association shown in FIG. 8 acquired in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12, the reference housing structure information "structure G", and the extracted audio information is audio X, the combination is associated with the node 61c. The node 61c is associated with a degree of risk of 30% with a degree of association w17 and a degree of risk of 70% with a degree of association w18. As a result of such a degree of association, the degree of danger at the time when the newly acquired image information and the approach information are actually acquired is obtained based on w17 and w18.

It should be noted that 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 are also referred to as the reference voice. The degree of association may be defined by further including the combination with information. Further, the degree of association may be defined by the combination of the reference image information and the reference audio information.

In FIG. 9, in addition to the above-mentioned reference image information and reference housing structure information, a combination of reference security service information and a degree of risk for the combination are set to three or more levels of association. An example is shown.

The reference security service information is related to whether or not the house has a contract for the security service of the security company, or the contract status regarding the specific contract contents. This contract is, for example, when a door is torn or a window glass is torn, a security guard comes to patrol the house from the security company.

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 housing structure information, and the reference security service information is the intermediate node nodes 61a to 61e as described above. Will be expressed as.

For example, in FIG. 8, in the node 61c, the reference image information P12 has an association degree w3, the reference housing structure information “structure G” has an association degree w7, and the reference security service information “uncontracted” has an association degree w7. It is related at w11.

Similarly, when such a degree of association is set, the degree of risk at the time when the newly acquired image information is actually acquired is determined based on the newly acquired image information, the housing structure information, and the security service information. Ask. The security service information referred to here is related to whether or not the house to be discriminated actually contracts the security service of the security company, or the contract status regarding the specific contract contents.

In determining the degree of risk, the degree of association shown in FIG. 9 acquired in advance is referred to. For example, if the acquired image is the same as or similar to the reference image information P12, the reference housing structure information "structure G", and the extracted security service information is "uncontracted", the combination is associated with the node 61c. The node 61c is associated with a risk level of 30% at a degree of association w17 and a risk level of 70% at a degree of association w18. As a result of such a degree of association, the degree of danger at the time when the newly acquired image information and the 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 the combination with the service information. Further, the degree of association may be defined by combining the reference image information and the reference security service information.

According to the present invention having the above-described configuration, it is possible to easily determine the risk of intrusion of a suspicious person in a house without requiring special skill and with a small amount of labor. Further, according to the present invention, it is possible to determine the degree of risk with higher accuracy than that performed by humans. 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, according to the present invention, it is characterized in that the optimum physical properties and the generation mechanism are searched for through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 5 stages, but is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more stages. It may be configured.

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

If this risk is high, the risk can be avoided by contacting a security service company or the police, or by immediately alerting the resident of the house. In particular, when the degree of danger is higher, safety can be protected by calling attention to the residents through voice or the like, or by generating a voice to the outside to discourage suspicious persons from invading. Further, according to the present invention, since the risk detection accuracy is high, it is sufficient to narrow down the call to the police only to the cases that are really necessary without calling the police unnecessarily.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. The user is warned that even an output discrimination result with an extremely low degree of association is connected as a slight sign, and may be useful as the discrimination result 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 lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. is there. On the other hand, if the threshold value is raised, there is a high possibility that the optimum risk level can be detected with a high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

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 and input parameters are based on camera images captured in a house, acquired reference image information, reference location information, reference home pattern information, reference time information, reference voice information, reference security service information, etc. When new findings are found regarding the relationship with the output solution (risk level), the degree of association is increased or decreased according to the findings.
In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.
Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, 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 the output data may be self-formed from the reading.

This update of the degree of association is artificially performed by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated automatically or automatically. Artificial intelligence may be utilized in these update processes.

1 Risk level discrimination system 2 Discrimination device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (9)

In the risk determination program for determining the risk of suspicious person invasion into a house
Acquisition of linkage degree to acquire in advance three or more levels of association between a combination having reference image information taken from the outside of the house and reference location information for identifying the location of the house and the degree of risk to the combination. Steps and
When newly determining the degree of risk, an information acquisition step of acquiring image information by newly photographing the outside of the house and acquiring location information indicating the location of the house, and
Based on the image information and location information acquired through the information acquisition step with reference to the association degree acquired in the association degree acquisition step, a computer performs a determination step for determining the risk of suspicious person intrusion into the house. A risk determination program characterized by being executed by a computer. In the above-mentioned association degree acquisition step, a combination having the reference person information obtained by extracting the person by further analyzing the reference image information and the degree of association with the risk degree for the combination are acquired in advance in three or more stages.
In the information acquisition step, when the degree of danger is newly determined, the person information obtained by extracting the person by analyzing the image information acquired by photographing the outside of the house is further acquired.
The risk determination program according to claim 1, wherein the determination step further determines the risk level based on the person information acquired through the information acquisition step. In the above-mentioned association degree acquisition step, three or more levels of association degree between the combination having the reference audio information recorded at the time of shooting the reference image information and the risk degree for the combination are acquired in advance.
In the information acquisition step, when newly determining the degree of danger, the recorded voice information is further acquired at the same time as the outside of the house is photographed.
The risk determination program according to claim 1 or 2, further comprising determining the risk level based on the voice information acquired through the information acquisition step. In the above-mentioned linkage degree acquisition step, the combination of the house having the reference security service information indicating the contract status of the security service of the security company and the risk level for the combination are acquired in advance at three or more levels.
In the information acquisition step, when the risk level is newly determined, the security service information indicating the contract status of the security service in the house is further acquired.
The risk determination program according to any one of claims 1 to 3, further comprising determining the risk level based on the security service information in the determination step. In the association degree obtaining step, further to the above combination, the three or more stages of association degree between the risk for the combination obtained in advance and a reference housing structure information containing structural information about the housing,
In the information acquisition step, when newly determining the degree of risk, the housing structure information of the house is acquired, and the information is acquired.
In the determination step, any one of claims 1 to 4 is characterized in that the risk of invasion of a suspicious person into the house is determined based on the housing structure information acquired through the information acquisition step. The risk determination program described in the section. In the association degree obtaining step further and the combination and a reference home pattern information indicating a home pattern resident of the house, previously obtained three or more stages association degree between the risk for the combination,
In the information acquisition step, when newly determining the degree of risk, the home pattern information indicating the home pattern of the house is acquired, and the home pattern information is acquired.
In the determination step, any one of claims 1 to 5, characterized in that the risk of invasion of a suspicious person into the house is determined based on the home pattern information acquired through the information acquisition step. The risk determination program described in the section. In the association degree obtaining step, further the combination and, in advance acquires the three stages or more association degree between the risk for the combination and a reference time zone information indicating a time period in the imaging time of the reference image information ,
In the information acquisition step, when the risk level is newly determined, the time zone information in the time zone at the time of the shooting is further acquired.
The risk determination program according to any one of claims 1 to 6, further comprising determining the risk of suspicious person invasion into the house based on the time zone information in the determination step. The risk determination according to any one of claims 1 to 8, wherein in the association degree acquisition step, the association degree corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. program. In a risk determination system for determining the risk of suspicious person intrusion into a house
Acquisition of linkage degree to acquire in advance three or more levels of association between a combination having reference image information taken from the outside of the house and reference location information for identifying the location of the house and the degree of risk to the combination. Means and
When newly determining the degree of risk, an information acquisition means for acquiring image information by newly photographing the outside of the house and acquiring location information indicating the location of the house,
A discriminating means for determining the risk of suspicious person invasion into the house based on the image information and the location information acquired through the information acquisition means with reference to the linkage degree acquired by the association degree acquisition means. A risk determination system characterized by being prepared.

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