Information processing device, information processing method, and recording medium

Abstract

An information processing device according to the present disclosure comprises an acquisition unit, a detection unit, and a warning unit. The acquisition unit acquires vibration data that is measured using an optical fiber that is laid in a monitoring area. The detection unit detects the transfer of an object to the monitoring area on the basis of the vibration data. The warning unit performs a warning process when the transfer of the object into the monitoring area is detected.

Description

Information processing device, information processing method, and recording medium

[0001] The present disclosure relates to an information processing device, an information processing method, and a program.

[0002] A technology related to this disclosure is disclosed in Patent Document 1. Patent Document 1 discloses a sensing technology using optical fibers. Specifically, Patent Document 1 discloses measuring the weight of an object based on data sensed by an optical fiber.

[0003] Special Publication No. 2022-507455

[0004] There is a need for a technology that can detect when a predetermined object (e.g., a prohibited item) is brought into a predetermined area (e.g., a prohibited area). In particular, there is a need for a technology that can perform such detection for a subject who appears randomly in various locations in a wide area. Patent Document 1 does not disclose this problem or a means for solving it.

[0005] One example of the objective of this disclosure is to provide a new technology for detecting the introduction of a predetermined object into a predetermined area.

[0006] According to one aspect of this disclosure, there is provided an information processing device having: an acquisition means for acquiring vibration data measured using optical fiber laid in a monitored area; a detection means for detecting the bringing of an object into the monitored area based on the vibration data; and a warning means for performing a warning process when the bringing of the object into the monitored area is detected.

[0007] Furthermore, according to one aspect of this disclosure, there is provided an information processing method in which one or more computers acquire vibration data measured using optical fiber laid in a monitored area, detect the bringing of an object into the monitored area based on the vibration data, and perform a warning process when the bringing of the object into the monitored area is detected.

[0008] Furthermore, according to one aspect of this disclosure, a program is provided that causes a computer to function as: an acquisition means that acquires vibration data measured using optical fiber laid in a monitored area; a detection means that detects the bringing of an object into the monitored area based on the vibration data; and a warning means that performs warning processing when the bringing of the object into the monitored area is detected.

[0009] According to one example of this disclosure, a new technology is realized for detecting the bringing of a predetermined object into a predetermined area.

[0010] FIG. 1 is a diagram showing an example of a functional block diagram of an information processing device. FIG. 2 is a flowchart showing an example of a processing flow of the information processing device. FIG. 3 is a diagram showing an example of a hardware configuration of the information processing device. FIG. 4 is a diagram showing an example of a monitoring area. FIG. 5 is a diagram showing another example of a monitoring area. FIG. 6 is a diagram showing another example of a monitoring area. FIG. 7 is a diagram showing an example of information processed by the information processing device. FIG. 8 is a diagram showing an example of information processed by the information processing device. FIG. 9 is a flowchart showing another example of a processing flow of the information processing device. FIG. 10 is a flowchart showing another example of a processing flow of the information processing device. FIG. 11 is a flowchart showing another example of a processing flow of the information processing device. FIG. 12 is a diagram showing another example of a functional block diagram of an information processing device.

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In this disclosure, the drawings relate to one or more embodiments. In all drawings, similar components are designated by similar reference numerals, and descriptions thereof will be omitted as appropriate.

[0012] First Embodiment Fig. 1 is a functional block diagram showing an overview of an information processing device 10. Fig. 2 is a flowchart showing an example of the flow of processing executed by the information processing device 10.

[0013] 1, the information processing device 10 includes an acquisition unit 11, a detection unit 12, and a warning unit 13. These functional units execute the process of the flowchart in FIG.

[0014] In S10, the acquisition unit 11 acquires vibration data measured using optical fiber laid in the monitored area. In S11, the detection unit 12 detects the bringing of an object into the monitored area based on the vibration data acquired in S11. In S12, the warning unit 13 performs warning processing if the bringing of an object into the monitored area is detected in S11.

[0015] As described above, the information processing device 10 of this embodiment has a feature of detecting the bringing of an object into a monitoring area based on vibration data measured within the monitoring area using "optical fiber."

[0016] Optical fiber itself acts as a sensor, so when measuring vibration data using optical fiber, sensing can be done seamlessly in a line rather than at a point. Therefore, by laying optical fiber widely in various positions within the monitored area, vibration data can be obtained from various positions within the monitored area.

[0017] The information processing device 10 detects the bringing of an object into the monitored area based on vibration data measured at various positions within the monitored area using such optical fiber. With such information processing device 10, it is possible to detect the bringing of a predetermined object from targets who appear indiscriminately at various positions within the monitored area.

[0018] In this way, the information processing device 10 realizes a new technique for detecting that a predetermined object has been brought into a predetermined area.

[0019] <<Second Embodiment>> <Overview> An information processing device 10 according to a second embodiment detects a vibration-generating object within a monitoring area based on vibration data measured using optical fiber, and estimates the weight of the vibration-generating object. Then, the information processing device 10 detects the bringing of an object into the monitoring area based on the estimated weight of the vibration-generating object. This will be described in detail below.

[0020] <Hardware Configuration> First, an example of the hardware configuration of the information processing device 10 will be described. Each functional unit of the information processing device 10 is realized by any combination of hardware and software. Those skilled in the art will understand that there are various variations in the realization method and device. The software includes programs that are pre-stored in the device before shipping, and programs downloaded from recording media such as CDs (Compact Discs) or servers on the Internet.

[0021] FIG. 3 is a block diagram illustrating an example of the hardware configuration of an information processing device 10. As shown in FIG. 3, the information processing device 10 has a processor 1A, a memory 2A, an input / output interface 3A, a peripheral circuit 4A, and a bus 5A. The peripheral circuit 4A includes various modules. The information processing device 10 does not necessarily have to have the peripheral circuit 4A. Note that the information processing device 10 may be composed of multiple devices that are physically and / or logically separated. In this case, each of the multiple devices may have the above hardware configuration.

[0022] The bus 5A is a data transmission path for the processor 1A, memory 2A, peripheral circuit 4A, and input / output interface 3A to mutually transmit and receive data. The processor 1A is, for example, a central processing unit (CPU) or a graphics processing unit (GPU). The memory 2A is, for example, a random access memory (RAM) or a read-only memory (ROM). The input / output interface 3A includes interfaces for acquiring information from input devices, external devices, external servers, external sensors, cameras, etc., and interfaces for outputting information to output devices, external devices, external servers, etc. The input / output interface 3A also includes an interface for connecting to a communication network such as the Internet. Examples of input devices include a keyboard, mouse, microphone, physical buttons, and touch panel. Examples of output devices include a display, projection device, speaker, printer, and mailer. The processor 1A can issue commands to each module and perform calculations based on the results of those calculations.

[0023] <Functional Configuration> Next, a detailed description will be given of the functional configuration of the information processing device 10. Fig. 1 is an example of a functional block diagram of the information processing device 10. As shown in the figure, the information processing device 10 has an acquisition unit 11, a detection unit 12, and a warning unit 13.

[0024] The acquisition unit 11 acquires vibration data measured using optical fibers laid in a monitoring area.

[0025] A "monitored area" is an area where bringing in objects is prohibited. The monitored area may be outdoors or indoors. There is no limit to the size of the monitored area.

[0026] An "object" is an object that is prohibited from being brought into a monitored area. The user of the information processing device 10 defines the object in advance. The object may be, for example, an illegal object such as a drug, a weapon such as a handgun, or something else.

[0027] An example of a monitoring area is shown in Fig. 4. In the example of Fig. 4, an optical fiber 1 is laid in a monitoring area D. The optical fiber 1 may be laid underground, for example. Although the optical fiber 1 shown in the figure is laid in a straight line, the method is not limited to this. The optical fiber 1 may also be laid in a curved line (not shown), or may be laid by other methods.

[0028] In the example of FIG. 4 , there is a monitoring area camera 2 that captures images of the monitoring area D. The number of monitoring area cameras 2 is not limited to the example shown. The monitoring area camera 2 may be fixed at a predetermined position as shown. Alternatively, although not shown, the monitoring area camera 2 may be mounted on a mobile body and capture images of various positions within the monitoring area D while moving. The mobile body may be a land mobile body that moves on land, an aircraft that moves in the air, an aquatic mobile body that moves on water, an underwater mobile body that moves underwater, or any other type. The mobile body has an autonomous movement mechanism and can move autonomously according to a predetermined algorithm. The mobile body may also be remotely controlled by a remote controller or the like.

[0029] The monitoring area camera 2 may generate images by detecting visible light, or may generate images by detecting other electromagnetic waves such as infrared light. The monitoring area camera 2 may capture moving images or still images. The monitoring area camera 2 may capture images using a wide-angle lens or an ultra-wide-angle lens, or may capture images using a so-called standard lens with an angle of view of, for example, around 47°.

[0030] Next, Fig. 5 shows a specific example of the monitoring area D in Fig. 4. Note that the specific example of the monitoring area D in Fig. 4 is not limited to the example in Fig. 5.

[0031] In the example of FIG. 5 , a monitoring area D and an observation area G are shown. An object R is present in the observation area G. The monitoring area D is an area that one passes through before entering the observation area G. For example, the monitoring area D is an area that one must pass through before entering the observation area G. In this example, for example, an act of bringing the object R present in the observation area G into the monitoring area D is detected.

[0032] 4, an optical fiber 1 is laid in a monitoring area D. In addition, at least one monitoring area camera 2 that captures images of the monitoring area D is present.

[0033] In the example of FIG. 5 , there is an observation area camera 3 that captures the observation area G. The number of observation area cameras 3 is not limited to the example shown. The observation area camera 3 may be fixed at a predetermined position as shown. Alternatively, although not shown, the observation area camera 3 may be mounted on a mobile body and capture images of various positions within the observation area G while moving. The mobile body may be a land mobile body that moves on land, an aircraft that moves in the air, an aquatic mobile body that moves on water, an underwater mobile body that moves underwater, or any other type. The mobile body has an autonomous movement mechanism and can move autonomously according to a predetermined algorithm. The mobile body may also be remotely controlled by a remote controller or the like.

[0034] The observation area camera 3 may generate images by detecting visible light, or may generate images by detecting other electromagnetic waves such as infrared light. The observation area camera 3 may capture moving images or still images. The observation area camera 3 may capture images using a wide-angle lens or an ultra-wide-angle lens, or may capture images using a so-called standard lens with an angle of view of, for example, about 47°.

[0035] Next, Fig. 6 shows a specific example of the monitoring area D and the observation area G in Fig. 5. Note that the specific example of the monitoring area D and the observation area G in Fig. 5 is not limited to the example in Fig. 6.

[0036] In the example of FIG. 6 , an object R exists in the sea. The object R is, for example, an illegal item such as a drug. The object R is tethered to a marker object (not shown), such as a floating buoy or a small boat. The object R is placed in such a location for the purpose of handing it over or storing it so that it is not discovered. A person attempting to bring the object R into the sea enters the sea from land, travels to the location of the object R, retrieves the object R, and brings it back to land. Note that a person bringing the object R onto land is thought to bring the object R into the sea in a state where it is not visible from the outside, such as by putting it in a bag or other personal belongings. Examples of means of transportation to the location of the object R tethered to the marker object include, but are not limited to, using a vehicle such as a small boat or a boat, or swimming.

[0037] In the example of FIG. 6 , the sea is the observation area G. The coastline (land) that is passed before entering the observation area G is the monitoring area D. In the example of FIG. 6 , an optical fiber 1 laid along the coastline is shown. Also shown is a monitoring area camera 2 that captures images of the coastline (monitoring area D). Furthermore, an observation area camera 3 that captures images of the sea (observation area G) is shown. In this example, the information processing device 10 can detect the act of bringing an object R placed in the sea (observation area G) into the coastline (monitoring area D).

[0038] The acquisition unit 11 acquires vibration data measured using optical fiber 1 laid in the monitoring area D. The acquisition unit 11 can also acquire monitoring area images which are images generated by the monitoring area camera 2. The acquisition unit 11 can also acquire observation area images which are images generated by the observation area camera 3. The vibration data, monitoring area images, and observation area images are accompanied by information specifying the date and time when each data was detected / generated.

[0039] The acquisition unit 11 preferably acquires vibration data, monitored area images, and observed area images in real time, but may also acquire data for a predetermined period of time in batch processing.

[0040] "Acquisition" includes at least one of the following: a device going to retrieve data or information stored in another device or storage medium (active acquisition); and a device inputting data or information output from another device (passive acquisition). Examples of active acquisition include making a request to another device and receiving a response, and accessing and reading information from another device or storage medium. An example of passive acquisition is receiving information that is distributed (or transmitted, push notification, etc.). Furthermore, acquisition may be selecting and acquiring from received data or information, or selecting and receiving distributed data or information.

[0041] Returning to FIG. 1, the detection unit 12 detects the bringing of an object R into the monitoring area D based on the vibration data and the monitoring area image.

[0042] The detection by the detection unit 12 is intended to broadly detect behavior (suspicious behavior) that is suspected with a certain degree of accuracy of being the bringing-in of the object R, and is not intended to detect behavior that can be identified with 100% accuracy as the bringing-in of the object R. Below, the principle of realizing such detection based on vibration data measured using the optical fiber 1 will be described.

[0043] In this detection, the weight of the belongings of a person present in the monitoring area D is estimated based on the vibration data. If the weight of the belongings meets a predetermined condition, the belongings (items contained in the belongings) are determined to be suspected of being the target object R.

[0044] If the belongings (items contained in the belongings) are object R, the weight of the belongings estimated based on the vibration data will be a value equivalent to the predefined range of weights for object R. Furthermore, if the belongings are object R, the weight of the belongings estimated based on the vibration data will be a value equivalent to the "weight of object R contained in the belongings" estimated from the volume of the belongings estimated by analyzing the image of the monitored area and the density of object R. The detection unit 12 can detect the bringing of object R into the monitored area D, for example, based on this relationship.

[0045] A specific example of detection based on the above principle will be described below. Note that the example described below is merely an example, and the detection unit 12 may employ other methods based on the above principle.

[0046] First, the detection unit 12 detects a vibration generating object within the monitoring area D based on vibration data measured within the monitoring area D. Then, the detection unit 12 estimates the weight of the vibration generating object based on the vibration data. Furthermore, the detection unit 12 estimates the position of the vibration generating object based on the vibration data.

[0047] A "vibration generating object" is an object that generates vibrations within the monitoring area D. For example, vibrations are generated within the monitoring area D by various actions such as walking, running, jumping, dragging an object, etc. The vibrations generated by the vibration generating object are detected by the optical fiber and appear in the vibration data.

[0048] The detection of the vibration generating object, the estimation of the weight of the vibration generating object, and the estimation of the position of the vibration generating object can be realized using well-known techniques. The weight of the vibration generating object can be estimated based on the magnitude of the vibration amplitude, for example.

[0049] Based on the results of processing the vibration data described above, the detection unit 12 can generate optical fiber sensing information as shown in Fig. 7. The optical fiber sensing information shown in the figure includes items such as time, position, and weight.

[0050] "Time" indicates the date and time when the vibration generating object was detected. "Location" indicates the location where the vibration generating object was detected. "Weight" indicates the estimated weight of the vibration generating object.

[0051] The detection unit 12 also analyzes the image within the monitoring area to detect people from within the monitoring area image and to detect belongings that the people are carrying. The detection unit 12 can detect people from within the monitoring area image using a well-known person detection technique.

[0052] The "possessions" to be detected are objects that can contain the object R. It is thought that a person who brings the object R into the monitoring area D will do so by placing the object R in a personal belonging such as a bag so that the object R cannot be seen from the outside. Therefore, the detection unit 12 detects possessions that are objects that can contain the object R from within the monitoring area image. Examples of possessions to be detected include, but are not limited to, bags, backpacks, cases, containers, bags, etc. The detection unit 12 can detect the above-mentioned possessions from within the monitoring area image using widely known image analysis techniques such as a classifier.

[0053] The detection unit 12 then estimates the capacity of the detected belongings. For example, the detection unit 12 may identify the characteristics of the belongings (type, manufacturer, brand name, product name, product number, etc.) through image analysis. The detection unit 12 may then identify the belongings based on the characteristics (identify the product number) and estimate the "maximum capacity" of the identified belongings as the "capacity of the detected belongings." The detection unit 12 may also identify the maximum capacity of the identified belongings based on information (information indicating the maximum capacity of each of multiple belongings) previously stored in the information processing device 10. Alternatively, the detection unit 12 may use technology such as a web search to search for and identify the maximum capacity of the identified belongings.

[0054] Additionally, the detection unit 12 may estimate the volume of the belongings at that time (when they are captured in the monitored area image) based on a three-dimensional image of the belongings. Some belongings change shape. The "volume of the belongings at that time" refers to the volume of the internal space of the belongings' shape at that time. Measuring the volume (volume) of an object displayed in a three-dimensional image can be achieved using widely known technologies. The three-dimensional image of the belongings can be generated using various methods. For example, the monitored area camera 2 may be a three-dimensional camera that generates three-dimensional images. The detection unit 12 may then process the three-dimensional image generated by the monitored area camera 2. Alternatively, the detection unit 12 may generate a three-dimensional image from a two-dimensional image generated by the monitored area camera 2. Generating a three-dimensional image from a two-dimensional image can be achieved using widely known technologies. For example, the detection unit 12 may generate a three-dimensional image from a two-dimensional image using an image generation tool generated by AI technology.

[0055] Based on the results of processing the monitored area image described above, the detection unit 12 can generate monitored area image information as shown in Fig. 8. The monitored area image information shown in the figure includes items such as a person ID (identifier), person appearance information, detection date and time, detection position, belongings appearance information, belongings capacity, and image.

[0056] The "person ID" is information for identifying multiple people detected in the monitoring area image from each other. Each person is assigned a unique person ID. The detection unit 12 can identify the same person who appears in the monitoring area image at multiple times using face recognition technology, tracking technology, etc.

[0057] "Person appearance information" is appearance information of each person extracted from the monitoring area image. The detection unit 12 analyzes the monitoring area image to generate person appearance information. The person appearance information includes, for example, face information (facial feature amounts, face image), physique information, skeleton information, height information, clothing information, hairstyle information, and other attributes (gender, age group, nationality, etc.).

[0058] The "detection date and time" indicates the date and time when each person was detected in the monitored area image. In other words, the detection date and time indicates the date and time when the monitored area image containing each person was captured.

[0059] The "personal item appearance information" is information about the appearance of the personal items of each person. The detection unit 12 analyzes the monitored area image to generate the personal item appearance information. The personal item appearance information includes, for example, the type, color, design, pattern, shape, manufacturer, brand name, product name, product number, etc. of the personal item.

[0060] The "belonging capacity" indicates the capacity of the belongings. For example, the belonging capacity may be the maximum capacity of the belongings or the current capacity of the belongings estimated based on the current shape of the belongings.

[0061] "Image" indicates the file name of the image of the monitored area generated at each detection date and time.

[0062] In this way, the monitoring area image information registers information about people who have entered and exited the monitoring area D. Such monitoring area image information makes it possible to manage people who have entered and exited the monitoring area D. The monitoring area image information may also indicate the time when each person entered the monitoring area D and the time when each person exited the monitoring area D.

[0063] The detection unit 12 can detect the bringing of an object R into the monitoring area D based on, for example, the above-mentioned optical fiber sensing information (see FIG. 7) and the monitoring area image information (see FIG. 8). The detection unit 12 can execute any one of the following detection processes 1 to 3.

[0064] (Detection Process 1) First, the detection unit 12 estimates the weight of the belongings at each timing based on the weight of the vibration-generating object at each timing indicated in the optical fiber sensing information (see FIG. 7 ). The vibration-generating object includes the person and the belongings. Therefore, for example, the detection unit 12 can estimate the weight of the belongings at each timing by subtracting the person's general weight (or the person's weight estimated by image analysis) from the weight of the vibration-generating object.

[0065] Furthermore, the detection unit 12 estimates the weight of the object R contained in the belongings, if any, based on the volume of the belongings at each timing indicated in the monitoring area image information and the density of the object R registered in advance. Specifically, the detection unit 12 can estimate the weight as the product of the volume of the belongings and the density of the object R.

[0066] The detection unit 12 then detects the bringing of object R into the monitoring area D based on the "weight of the belongings estimated based on the optical fiber sensing information" and the "weight of the object R contained in the belongings, if any, estimated based on the monitoring area image." The detection unit 12 time-synchronizes the optical fiber sensing information and the monitoring area image information, and detects the bringing of object R into the monitoring area D at that time based on the data at the same time (the two weights).

[0067] If the similarity between the two weights is equal to or greater than a threshold, there is a suspicion that the belongings contain an object R. Therefore, if the similarity between the two weights is equal to or greater than a threshold, the detection unit 12 detects that there is a suspicion that the object R has been brought into the monitoring area D.

[0068] On the other hand, if the similarity between the two weights is less than the threshold, there is little suspicion that the belongings contain the object R. Therefore, if the similarity between the two weights is less than the threshold, the detection unit 12 does not detect that the object R is suspected to have been brought into the monitoring area D.

[0069] The similarity between the two weights may be calculated using a predetermined calculation model (such as an arithmetic formula) based on the difference between the two weights or the rate of change from one to the other. When the calculation model receives input of the two weights, it outputs the similarity between the two weights. The calculation model is configured to output a larger similarity as the difference or rate of change becomes smaller.

[0070] (Detection Process 2) First, the detection unit 12 estimates the weight of the belongings at each timing based on the weight of the vibration generating object at each timing indicated by the optical fiber sensing information. For example, the detection unit 12 can estimate the weight of the belongings by subtracting the person's general weight (or the person's weight estimated by image analysis) from the weight of the vibration generating object.

[0071] Then, the detection unit 12 determines whether the estimated weight of the carried item at each timing is within a predefined range of the weight of the object R.

[0072] If the estimated weight of the belongings falls within a predefined range of the weight of the object R, the belongings are suspected of containing the object R. Therefore, if the estimated weight of the belongings falls within a predefined range of the weight of the object R, the detection unit 12 detects that the object R is suspected of being brought into the monitoring area D.

[0073] The weight of the object R may vary depending on the amount of the object R. In this case, the amount (a certain amount) for which it is desired to detect the carry-in behavior can be defined in advance as the weight of the object R. Also, a typical weight of the object R carried in, estimated from past cases, can be defined in advance as the weight of the object R.

[0074] (Detection Process 3) The detection unit 12 detects the bringing of the object R into the monitoring area D by combining the detection processes 1 and 2.

[0075] Specifically, when both detection processes 1 and 2 detect that there is a suspicion that an object R has been brought into the monitoring area D, the detection unit 12 detects that there is a suspicion that an object R has been brought into the monitoring area D.

[0076] In addition, the detection unit 12 may detect that there is a suspicion of bringing an object R into the monitoring area D if at least one of detection processes 1 and 2 detects that there is a suspicion of bringing an object R into the monitoring area D.

[0077] Returning to FIG. 1, when the detection unit 12 detects that the object R has been brought into the monitoring area D, the warning unit 13 performs a warning process.

[0078] The warning unit 13 can output warning information via an output device such as a display, a speaker, a warning lamp, or a vibrator. For example, the warning unit 13 may output warning information via a display or a speaker. The warning unit 13 may also output a warning sound via a speaker. The warning unit 13 may also turn on a warning lamp or vibrate using a vibrator as a warning process. Alternatively, the warning unit 13 may transmit warning information to a pre-registered external device.

[0079] The warning information indicates the content of the warning. For example, the warning information indicates that the detection unit 12 has detected the bringing of the object R into the monitoring area D. The warning information may further indicate the position where the bringing of the object R was detected. The warning information may also include an image of the monitoring area generated at the time the bringing of the object R was detected.

[0080] Next, an example of the flow of processing by the information processing device 10 will be described using the flowchart in Figure 9. Note that the purpose here is to explain the flow of processing. Since the details of each process have been described above, the description here will be omitted as appropriate.

[0081] The information processing device 10 acquires vibration data measured using optical fiber laid in the monitoring area D and a monitoring area image captured of the monitoring area D (S20).

[0082] Next, the information processing device 10 detects a vibration generating object within the monitoring area D based on the acquired vibration data, and estimates the weight of the vibration generating object (S21).

[0083] The information processing device 10 also detects belongings carried by the person from the acquired monitoring area image and estimates the volume of the belongings (S22). Next, the information processing device 10 estimates the weight of the object R contained in the belongings if the object R is contained in the belongings based on the density of the object R and the estimated volume of the belongings (S23).

[0084] Next, the information processing device 10 detects that the object R has been brought into the monitoring area D based on the estimated weight of the object R when it is contained in the belongings and the estimated weight of the vibration generating object (S24). Specifically, the information processing device 10 detects that the object R has been brought into the monitoring area D when the similarity between the two estimated results is equal to or greater than a threshold value.

[0085] Then, when the information processing device 10 detects that the object R has been brought into the monitoring area D, it performs a warning process (S25).

[0086] <Effects> The information processing device 10 of the second embodiment can achieve the same effects as the information processing device 10 of the first embodiment. Furthermore, the information processing device 10 can estimate the weight of a vibration generating object present in the monitoring area D based on vibration data measured using optical fiber, and determine whether the vibration generating object is the object R based on the weight. If it is determined that the vibration generating object is the object R, it can detect the bringing in of the object R.

[0087] For example, the information processing device 10 can make the above determination based on whether or not the weight of the vibration generating object is within a range of the weight of the target object R that is defined in advance.

[0088] Additionally, the information processing device 10 can make the above determination based on the weight of the vibration generating object and the analysis results of the monitoring area image captured within the monitoring area. For example, the information processing device 10 estimates the weight of the object R contained in the belongings if the object R is contained in the belongings based on the volume of the belongings detected in the monitoring area image and the density of the object R. Then, the information processing device 10 can make the above determination based on whether the weight of the vibration generating object is equivalent to the weight of the object R contained in the belongings if the object R is contained in the belongings.

[0089] Additionally, the information processing device 10 can perform the above determination by combining the above methods.

[0090] For example, when detecting the act of bringing an object R from the sea onto land as in the example of FIG. 6 , it is necessary to detect this act separately from the act of catching seafood in the sea and bringing it onto land (fishing). Seafood tends to contain a lot of water and be heavy. As a result, the weight of the belongings tends to be heavier than the object R. Furthermore, the density of the belongings tends to be greater than that of the object R. According to the information processing device 10 that detects the bringing in of the object R based on the weight of the vibration-generating object as described above, it is possible to detect the bringing in of the object R separately from the bringing in of seafood.

[0091] 6 , when a monitoring area D is set along the seashore and the act of bringing an object R along the coast is to be detected, it is necessary to detect the object R separately from the bringing in of leisure goods such as swim rings, goggles, etc. Leisure goods vary in weight and density, and some of these goods may have weights and densities that differ from those of the object R. As described above, the information processing device 10 that detects the bringing in of the object R based on the weight of the vibration-generating object can detect the bringing in of the object R by distinguishing it from the bringing in of leisure goods with a certain degree of accuracy.

[0092] According to such an information processing device 10, it is possible to detect the bringing of an object R into the monitoring area D with a certain degree of accuracy based on vibration data measured using optical fiber.

[0093] <<Third Embodiment>> <Overview> An information processing device 10 of a third embodiment detects, based on a monitoring area image captured of the monitoring area D, that a person who has left the monitoring area D has returned to the monitoring area D. The information processing device 10 then detects the bringing in of an object R into the monitoring area D based on the difference between data (at least one of vibration data and monitoring area image) before the person left the monitoring area and data after the person returned to the monitoring area D. This will be described in detail below.

[0094] <Hardware Configuration> The hardware configuration of the information processing apparatus 10 of the third embodiment can be the same as the hardware configuration described in the second embodiment.

[0095] <Functional Configuration> Next, the functional configuration of the information processing device 10 will be described in detail. Fig. 1 is an example of a functional block diagram of the information processing device 10. As shown in the figure, the information processing device 10 has an acquisition unit 11, a detection unit 12, and a warning unit 13. The configurations of the acquisition unit 11 and the warning unit 13 can be the same as those in the first and second embodiments.

[0096] The detection unit 12 first detects a first person based on a monitoring area image obtained by capturing the monitoring area D.

[0097] A "first person" is a person who leaves the monitoring area D and then returns to the monitoring area D. The detection unit 12 can determine, for example, by using face recognition technology, whether the person appearing in each of a plurality of monitoring area images taken at different times is the same person. Based on the determination result, the detection unit 12 can detect that a person detected within the monitoring area D has left the monitoring area D and then returned to the monitoring area D again.

[0098] The first person may be a person who leaves the monitoring area D, enters the observation area G, and then returns to the monitoring area D. Such detection of the first person can be achieved by using widely known technology. For example, the detection unit 12 can detect that the person has left the monitoring area D and entered the observation area G based on the movement trajectory within the monitoring area image, the position where the person was framed out within the monitoring area image, the direction of movement when the person was framed out, etc. The detection unit 12 can then detect that the person has returned to the monitoring area D from the observation area G based on the movement trajectory within the monitoring area image, the position where the person was framed in within the monitoring area image, the direction of movement when the person was framed in, etc.

[0099] After detecting the first person, the detection unit 12 identifies the first timing and the second timing based on the monitored area image.

[0100] The "first timing" is the timing at which the first person is detected in the monitoring area image before leaving the monitoring area D.

[0101] The "second timing" is the timing at which a first person is detected in the monitoring area image after leaving the monitoring area D and then returning to the monitoring area D. Note that the second timing may also be the timing at which a first person is detected in the monitoring area image after leaving the monitoring area D, entering the observation area G, and then returning to the monitoring area D.

[0102] Then, the detection unit 12 detects the bringing of an object into the monitoring area D based on the difference between the vibration data at the first timing and the vibration data at the second timing.

[0103] Specifically, the detection unit 12 estimates the weight of the vibration generating object based on the vibration data at the first timing. The detection unit 12 also estimates the weight of the vibration generating object based on the vibration data at the second timing. The detection unit 12 then calculates the difference (amount of change) between the weight of the vibration generating object based on the vibration data at the first timing and the weight of the vibration generating object based on the vibration data at the second timing.

[0104] The vibration generating object is the first person, and the weight of the vibration generating object is the weight of the first person. The weight of the first person is the sum of the first person's body weight and the weight of the first person's belongings. Therefore, the calculated difference is the difference between the weight of the first person at the first timing and the weight of the first person at the second timing. In other words, it is the difference between the weight of the first person before leaving the monitoring area D and the weight of the first person after returning to the monitoring area D. This weight difference indicates the weight of an object brought into the monitoring area D by the first person who left the monitoring area D. The detection unit 12 detects the bringing of an object R into the monitoring area D based on the weight of such an object. Specifically, the detection unit 12 can execute any of the following detection processes 4 to 7.

[0105] (Detection Process 4) The detection unit 12 estimates the volume of the belongings shown in the surveillance area image generated at the second timing using a process similar to detection process 1. Next, the detection unit 12 estimates the weight of the object R contained in the belongings, if any, based on the estimated volume of the belongings and the density of the object R registered in advance. Specifically, the detection unit 12 can estimate the weight as the product of the volume of the belongings and the density of the object R.

[0106] Then, the detection unit 12 detects the bringing of object R into the monitoring area D based on the ``weight of the object being brought in'' estimated based on the vibration data as described above and the ``weight of the object contained in the belongings if object R is contained in the belongings estimated based on the monitoring area image.''

[0107] If the similarity between the two weights is equal to or greater than a threshold, there is a suspicion that the belongings contain an object R. Therefore, if the similarity between the two weights is equal to or greater than a threshold, the detection unit 12 detects that there is a suspicion that the object R has been brought into the monitoring area D.

[0108] On the other hand, if the similarity between the two weights is less than the threshold, there is little suspicion that the belongings contain the object R. Therefore, if the similarity between the two weights is less than the threshold, the detection unit 12 does not detect that the object R is suspected to have been brought into the monitoring area D.

[0109] The similarity between the two weights may be calculated using a predetermined calculation model (such as an arithmetic formula) based on the difference between the two weights or the rate of change from one to the other. When the calculation model receives input of the two weights, it outputs the similarity between the two weights. The calculation model is configured to output a larger similarity as the difference or rate of change becomes smaller.

[0110] (Detection Process 5) The detection unit 12 estimates the volume of belongings of the person detected from the surveillance area image at the first timing. The detection unit 12 also estimates the volume of belongings of the person detected from the surveillance area image at the second timing. The detection unit 12 can estimate the volume of belongings at each of the first timing and the second timing using a method similar to the "method for estimating the volume of belongings at that time" described in the second embodiment.

[0111] The detection unit 12 then estimates the difference (amount of change) between the volume of the belongings at the first timing and the volume of the belongings at the second timing as the volume of the brought-in object contained in the belongings. Next, the detection unit 12 estimates the weight of the brought-in object contained in the belongings when the object R is contained in the belongings (when the brought-in object is object R) based on the estimated volume of the brought-in object and the density of the object R that has been registered in advance. Specifically, the detection unit 12 can estimate the weight as the product of the volume of the brought-in object and the density of the object R.

[0112] Then, the detection unit 12 detects the bringing of object R into the monitoring area D based on the ``weight of the object being brought in'' estimated based on the vibration data using the method described in detection process 4 and the ``weight of object R contained in the belongings if object R is contained in the belongings estimated based on the monitoring area image.''

[0113] If the similarity between the two weights is equal to or greater than a threshold, there is a suspicion that the belongings contain an object R. Therefore, if the similarity between the two weights is equal to or greater than a threshold, the detection unit 12 detects that there is a suspicion that the object R has been brought into the monitoring area D.

[0114] On the other hand, if the similarity between the two weights is less than the threshold, there is little suspicion that the belongings contain the object R. Therefore, if the similarity between the two weights is less than the threshold, the detection unit 12 does not detect that the object R is suspected to have been brought into the monitoring area D.

[0115] The similarity between the two weights may be calculated using a predetermined calculation model (such as an arithmetic formula) based on the difference between the two weights or the rate of change from one to the other. When the calculation model receives input of the two weights, it outputs the similarity between the two weights. The calculation model is configured to output a larger similarity as the difference or rate of change becomes smaller.

[0116] (Detection Process 6) The detection unit 12 determines whether the "weight of the carried object" estimated based on the vibration data by the method described in Detection Process 4 is within a predefined range of the weight of the target object R.

[0117] If the estimated weight of the brought-in object falls within the predefined range of weights of the target object R, the brought-in object is suspected to be the target object R. Therefore, if the estimated weight of the brought-in object falls within the predefined range of weights of the target object R, the detection unit 12 detects that there is a suspicion that the target object R has been brought into the monitoring area D.

[0118] (Detection Process 7) The detection unit 12 detects the bringing of the object R into the monitoring area D by combining the detection processes 4 to 6.

[0119] Specifically, if the detection unit 12 detects that there is a suspicion that an object R has been brought into the monitoring area D in all of detection processes 4 to 6, it detects that there is a suspicion that an object R has been brought into the monitoring area D.

[0120] In addition, the detection unit 12 may detect that there is a suspicion of bringing an object R into the monitoring area D if at least one of the detection processes 4 to 6 detects that there is a suspicion of bringing an object R into the monitoring area D.

[0121] Next, an example of the flow of processing by the information processing device 10 will be described using the flowchart in Fig. 10. Note that the purpose here is to explain the flow of processing. Details of each process have been described above, so explanations here will be omitted as appropriate.

[0122] The information processing device 10 acquires vibration data measured using optical fiber laid in the monitoring area D and a monitoring area image captured of the monitoring area D (S30).

[0123] Next, the information processing device 10 identifies the first timing and the second timing based on the monitoring area image (S31).

[0124] The first timing is the timing at which the first person is detected in the monitoring area image before leaving the monitoring area D. The second timing is the timing at which the first person is detected in the monitoring area image after leaving the monitoring area D and returning to the monitoring area D. Note that the second timing may also be the timing at which the first person is detected in the monitoring area image after leaving the monitoring area D, entering the observation area G, and then returning to the monitoring area D.

[0125] The first person is a person who leaves the monitoring area D and then returns to the monitoring area D. Note that the first person may also be a person who leaves the monitoring area D, enters the observation area G, and then returns to the monitoring area D.

[0126] Next, the detection unit 12 estimates the amount of change in weight based on the difference between the vibration data at the first timing and the vibration data at the second timing (S32). Specifically, the detection unit 12 estimates the weight of the vibration generating object based on the vibration data at the first timing. The detection unit 12 also estimates the weight of the vibration generating object based on the vibration data at the second timing. The detection unit 12 then calculates the difference between the weight of the vibration generating object based on the vibration data at the first timing and the weight of the vibration generating object based on the vibration data at the second timing. This weight difference is the difference between the weight of the first person before leaving the monitoring area D and the weight of the first person after returning to the monitoring area D. More specifically, this weight difference indicates the weight of an object brought into the monitoring area D by the first person who left the monitoring area D.

[0127] The information processing device 10 also detects belongings carried by the person from the acquired monitoring area image and estimates the volume of the belongings (S33). Next, the information processing device 10 estimates the weight of the object R contained in the belongings if the object R is contained in the belongings based on the density of the object R and the estimated volume of the belongings (S34).

[0128] Next, the information processing device 10 detects that the object R has been brought into the monitoring area D based on the estimated weight of the object R when it is contained in the belongings and the estimated weight of the brought-in object (S35). Specifically, the information processing device 10 detects that the object R has been brought into the monitoring area D if the similarity between the two estimated results is equal to or greater than a threshold.

[0129] Then, when the information processing device 10 detects that the object R has been brought into the monitoring area D, it performs a warning process (S36).

[0130] Next, another example of the processing flow of the information processing device 10 will be described using the flowchart in Fig. 11. Note that the purpose here is to explain the processing flow. Since the details of each process have been described above, the description here will be omitted as appropriate.

[0131] The information processing device 10 acquires vibration data measured using optical fiber laid in the monitoring area D and a monitoring area image captured of the monitoring area D (S40).

[0132] Next, the information processing device 10 identifies the first timing and the second timing based on the monitoring area image (S41).

[0133] The first timing is the timing at which the first person is detected in the monitoring area image before leaving the monitoring area D. The second timing is the timing at which the first person is detected in the monitoring area image after leaving the monitoring area D and returning to the monitoring area D. Note that the second timing may also be the timing at which the first person is detected in the monitoring area image after leaving the monitoring area D, entering the observation area G, and then returning to the monitoring area D.

[0134] The first person is a person who leaves the monitoring area D and then returns to the monitoring area D. Note that the first person may also be a person who leaves the monitoring area D, enters the observation area G, and then returns to the monitoring area D.

[0135] Next, the detection unit 12 estimates a weight difference (amount of change) based on the difference between the vibration data at the first timing and the vibration data at the second timing (S42). Specifically, the detection unit 12 estimates the weight of the vibration generating object based on the vibration data at the first timing. The detection unit 12 also estimates the weight of the vibration generating object based on the vibration data at the second timing. The detection unit 12 then calculates the difference between the weight of the vibration generating object based on the vibration data at the first timing and the weight of the vibration generating object based on the vibration data at the second timing. This weight difference is the difference between the weight of the first person before leaving the monitoring area D and the weight of the first person after returning to the monitoring area D. More specifically, this weight difference indicates the weight of an object brought into the monitoring area D by the first person who left the monitoring area D.

[0136] The information processing device 10 also estimates a difference (amount of change) in the volume of the first person's belongings detected from the surveillance area image at the first timing and the first person's belongings detected from the surveillance area image at the second timing (S43). Specifically, the detection unit 12 estimates the current volume of the object appearing in the surveillance area image at the first timing. The detection unit 12 also estimates the current volume of the object appearing in the surveillance area image at the second timing. The detection unit 12 then calculates the difference in volume between the current volume of the object appearing in the surveillance area image at the first timing and the current volume of the object appearing in the surveillance area image at the second timing. This volume difference indicates the volume of an object brought into the surveillance area D by the first person who left the surveillance area D.

[0137] Next, the information processing device 10 estimates the weight of the object R contained in the belongings when the object R is contained in the belongings (when the brought-in object is the object R) based on the density of the object R and the estimated change in the capacity of the belongings (volume of the brought-in object) (S44).

[0138] Next, the information processing device 10 detects that the object R has been brought into the monitoring area D based on the estimated weight of the object R when it is contained in the belongings and the estimated weight of the brought-in object (S45). Specifically, the information processing device 10 detects that the object R has been brought into the monitoring area D when the similarity between the two estimated results is equal to or greater than a threshold.

[0139] Then, when the information processing device 10 detects that the object R has been brought into the monitoring area D, it performs a warning process (S46).

[0140] Other configurations of the information processing device 10 can be the same as those in the first and second embodiments.

[0141] <Effects> According to the information processing device 10 of the third embodiment, it is possible to achieve the same effects as those of the information processing device 10 of the first and second embodiments.

[0142] Furthermore, the information processing device 10 detects that a person who has left the monitoring area D has returned to the monitoring area D. Then, the information processing device 10 can detect the bringing of an object R into the monitoring area D based on the difference between the data (at least one of the vibration data and the monitoring area image) before the person left the monitoring area and the data after the person returned to the monitoring area D.

[0143] For example, the information processing device 10 can estimate the amount of change in the person's weight as the weight of an object brought into the monitoring area D by the person. Then, the information processing device 10 can detect the bringing of an object R into the monitoring area D based on the estimated weight of the object.

[0144] Furthermore, the information processing device 10 can estimate the amount of change in the volume of the belongings as the volume of the object brought in by the person into the monitoring area D. Then, the information processing device 10 can detect the bringing in of the target object R into the monitoring area D based on the estimated result of the volume of the brought-in object.

[0145] Furthermore, the information processing device 10 can detect the bringing of an object R into the monitoring area D based on the estimated weight and volume of the brought-in object. For example, if the density of the brought-in object indicated by the estimated weight and volume of the brought-in object is similar to the density of the object R by a predetermined level or more, the information processing device 10 can detect that an object R has been brought into the monitoring area D.

[0146] According to the information processing device 10, it is possible to detect the bringing in of the object R with a certain degree of accuracy for subjects who appear indiscriminately at various positions in a wide area.

[0147] <<Fourth Embodiment>> <Overview> An information processing device 10 of the fourth embodiment detects an object R from an observation area image captured of an observation area G (see FIG. 5 ). Then, based on the detection result, the information processing device 10 adjusts the shooting position of a monitoring area camera 2 that captures an image of a monitoring area D. This will be described in detail below.

[0148] <Hardware Configuration> The hardware configuration of the information processing apparatus 10 of the fourth embodiment can be the same as the hardware configuration described in the second embodiment.

[0149] <Functional Configuration> Next, the functional configuration of the information processing device 10 will be described in detail. Fig. 12 is an example of a functional block diagram of the information processing device 10. As shown in the figure, the information processing device 10 has an acquisition unit 11, a detection unit 12, a warning unit 13, and an adjustment unit 14. The configurations of the acquisition unit 11 and the warning unit 13 can be the same as those of the first to third embodiments.

[0150] The detection unit 12 detects the object R from an observation area image obtained by capturing the observation area G. The detection unit 12 can realize the detection of the object R by using a well-known image analysis technique.

[0151] For example, the feature amount of the appearance of the object R may be stored in advance in the information processing device 10. Then, the detection unit 12 may detect the object R in the observation area image based on the feature amount of the appearance of the object R.

[0152] Additionally, as described in the second embodiment with reference to FIG. 6 , the object R may be present in the observation area G while being tethered to a landmark object (not shown), such as a buoy or a small boat floating in the sea. In this case, the information processing device 10 may store in advance the appearance feature amount of the landmark object. The detection unit 12 may then detect the object R by detecting the landmark object in the observation area image based on the appearance feature amount of the landmark object. In this case, the detection unit 12 can detect the position of the landmark object as the position of the object R.

[0153] The adjustment unit 14 adjusts the shooting position of the monitoring area camera 2 that shoots the monitoring area D based on the position of the object R detected in the observation area image. The “shooting position” is the position at which the monitoring area camera 2 shoots the image.

[0154] The adjustment unit 14 determines a position where the monitoring area camera 2 should take an image based on the position of the object R detected in the observation area image. Then, the adjustment unit 14 adjusts the image taking position of the monitoring area camera 2 so that the determined position is included in the image taking position. The adjustment of the image taking position of the monitoring area camera 2 can be realized using widely known technology.

[0155] Here, a process for determining the position at which the monitoring area camera 2 is to take an image based on the position of the object R detected in the observation area image will be described.

[0156] For example, a model is generated in advance that determines a shooting point in the monitoring area D from one point in the observation area G. The adjustment unit 14 can input the position of the object R into this model, thereby determining the position (shooting point) at which the monitoring area camera 2 will take an image.

[0157] There are various algorithms for this model. For example, this model may be configured to determine a point in the monitoring area D that is the shortest distance from a point in a specified observation area G as the shooting point in the monitoring area D.

[0158] Other configurations of the information processing device 10 can be the same as those of the first to third embodiments.

[0159] <Operational Effects> According to the information processing device 10 of the fourth embodiment, it is possible to achieve the same operational effects as the information processing devices 10 of the first to third embodiments.

[0160] Furthermore, the information processing device 10 can detect the object R from within the observation area image captured of the observation area G, and based on the detection result, adjust the shooting position of the monitoring area camera 2 capturing the image of the monitoring area D. Such an information processing device 10 makes it possible to capture with high accuracy, with the monitoring area camera 2, an image of a person positioned in the monitoring area D who is going to retrieve the object R present in the observation area G. As a result, it becomes possible to execute the "processing using the monitoring area image" described in the first to third embodiments.

[0161] <<Modifications>> Below, modifications that can be applied to the information processing apparatus 10 of the first to fourth embodiments will be described. These modifications can also achieve the same effects as the information processing apparatus 10 of the first to fourth embodiments.

[0162] <Modification 1> The detection unit 12 can detect collective behavior within the monitoring area D based on vibration data measured using the optical fiber 1. Specifically, the detection unit 12 calculates the distance between multiple vibration generating objects detected at the same time. Then, if the distance between the multiple vibration generating objects is equal to or less than a threshold, the detection unit 12 determines that the multiple vibration generating objects are engaging in collective behavior. Alternatively, the detection unit 12 may determine that the multiple vibration generating objects are engaging in collective behavior if the state in which the distance between the multiple vibration generating objects is equal to or less than the threshold continues for a predetermined time or more.

[0163] The warning unit 13 can perform the warning process when group behavior is detected.

[0164] <Modification 2> After detecting the object R in the observation area image, the detection unit 12 continues to monitor the object R. Then, the detection unit 12 can detect an object approaching the object R based on the observation area image.

[0165] When an object approaching the target object R is detected, the warning unit 13 can perform the warning process.

[0166] In addition, upon detecting an object approaching the target object R, the detection unit 12 may start the "process of detecting the bringing of the target object R into the monitoring area D based on the vibration data and the monitoring area image" described in the first to third embodiments.

[0167] <Modification 3> Based on features appearing in the vibration data, the detection unit 12 may detect the bringing of the object R into the monitoring area D. For example, the detection unit 12 may detect the bringing of the object R into the monitoring area D using a learning model that has been machine-learned from vibration data obtained when the object R is brought into the monitoring area D.

[0168] <Modification 4> The information processing device 10 can acquire data of at least one of the monitoring area D and the observation area G obtained by an infrared thermo camera, a sonar, a moisture detector, an active acoustic sensor, a passive acoustic sensor, etc. The information processing device 10 can then use the data to detect the bringing of an object R into the monitoring area D.

[0169] For example, the detection unit 12 may detect an object (such as a person) present in the monitoring area D based on a sound detected by an acoustic sensor. The acoustic sensor may be, for example, an optical fiber, but is not limited to this.

[0170] The detection unit 12 may also detect an object R present in the observation area G based on sonar data. For example, the detection unit 12 may detect an object (including natural objects and man-made objects) present in the observation area G based on the sonar data. The detection unit 12 may then estimate the size of the detected object based on the sonar data. The detection unit 12 may then analyze an image of the object captured by an infrared thermal camera to estimate the contents of the object.

[0171] <Variation 5> The information processing device 10 may detect that a new object has been detected in the observation area G by detecting a difference between the observation area image and a background image of the observation area G. The information processing device 10 may then save the observation area image for a predetermined time before and after the timing at which the new object was detected in the observation area G.

[0172] <Variation 6> As described in the second embodiment, the monitoring area image information registers information about people who have entered and exited the monitoring area D. The monitoring area image information can indicate, for example, the time when each person entered the monitoring area D and the time when each person exited the monitoring area D.

[0173] The information processing device 10 counts the number of visitors per unit time based on such monitored area image information, and can perform the above-mentioned warning process if the number of visitors satisfies the warning condition. The warning condition may be, but is not limited to, "the number of visitors per unit time is equal to or greater than a first threshold" or "the number of visitors per unit time has increased by equal to or greater than a second threshold compared to the number of visitors per unit time immediately preceding it." The first threshold is defined by the number of visitors. The second threshold is defined by the increased number of visitors or the rate of increase.

[0174] <Variation 7> When the information processing device 10 detects that an object R has been brought into the monitoring area D, it can save a monitoring area image in which the person who brought the object R and the object R are captured for a predetermined period of time.

[0175] <Modification 8> The information processing device 10 can create and output a report on the detection result of the bringing of the object R into the monitoring area D.

[0176] The information processing device 10 can include various processed information in a report. For example, the report may include the date and time when the bringing of the object R into the monitoring area D was detected, and an image of the monitoring area at that date and time. The report may also include the weight of the vibration-generating object, the weight of the belongings, the weight of the brought-in object, etc., estimated from the vibration data. The report may also include the location of the vibration-generating object. The report may also include an image of the monitoring area including the person and their belongings who brought the object R. The report may also include an image of the person and their belongings cut out from the monitoring area image including the person and their belongings who brought the object R. The report may also include an observation area image including the object R present in the observation area G. The report may also include an observation area image including the object R present in the observation area G and an object (person) approaching the object R. The report may also include an observation area image taken when the object R first appeared in the observation area G.

[0177] The information processing device 10 may output the report via an output device (such as a display or a speaker). The information processing device 10 may also transmit the report to a pre-registered external device. For example, the report may be transmitted to a mobile terminal carried by a police officer, a security guard, a patrol officer, or the like.

[0178] Although this disclosure has been described above with reference to the embodiments, this disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of this disclosure within the scope of this disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0179] In addition, in the flowcharts used in the above description, multiple steps (processes) are described in order. However, the order of the steps performed in each embodiment is not limited to the order described. In each embodiment, the order of the steps shown in the drawings can be changed as long as it does not cause any problems in terms of the content.

[0180] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes: 1. An information processing device comprising: an acquisition means for acquiring vibration data measured using optical fiber laid in a monitored area; a detection means for detecting the bringing of an object into the monitored area based on the vibration data; and a warning means for performing warning processing when the bringing of the object into the monitored area is detected. 2. The information processing device described in 1, wherein the detection means: detects a vibration-generating object in the monitored area based on the vibration data and estimates the weight of the vibration-generating object; detects possessions carried by a person from a monitored area image captured of the monitored area and estimates the volume of the possessions; and detects the bringing of the object into the monitored area based on the weight of the vibration-generating object and the volume of the possessions. 3. 1. The information processing device of claim 1, wherein the detection means estimates a weight of the object contained in the person's belongings if the object is contained in the person's belongings based on the density of the object and the volume of the person's belongings, and detects the person's bringing into the monitored area based on the estimated weight of the vibration-generating object and the estimated weight of the object contained in the person's belongings. 4. The information processing device of any of claims 1 to 3, wherein the detection means detects the person's bringing into the monitored area based on a difference between the vibration data before the person leaves the monitored area and the vibration data after the person has left the monitored area and returned to the monitored area. 5. The information processing device of claim 1, wherein the detection means identifies a first timing at which the person is detected before leaving the monitored area and a second timing at which the person is detected after leaving the monitored area and returning to the monitored area, based on a monitored area image captured of the monitored area, and detects the person's bringing into the monitored area based on the difference between the vibration data at the first timing and the vibration data at the second timing.6. The information processing device described in 5, wherein the detection means estimates a change in volume between the person's belongings detected from the monitored area image at the first timing and the belongings detected from the monitored area image at the second timing, estimates a change in weight based on a difference between the vibration data at the first timing and the vibration data at the second timing, and detects the object being brought into the monitored area based on the change in volume and the change in weight of the belongings. 7. The information processing device described in 6, wherein the detection means estimates a weight of the object contained in the belongings if the object is contained in the belongings based on the density of the object and the change in volume of the belongings, and detects the object being brought into the monitored area based on the estimated weight of the object contained in the belongings and the change in weight. 8. 8. An information processing device according to any one of 2, 3, 5, 6 and 7, wherein the monitored area is an area passed through before entering an observation area where the object is present, and the detection means detects the object from an observation area image captured of the observation area, and has an adjustment means for adjusting the shooting position of a monitored area camera that captures the monitored area based on the position of the object detected in the observation area image. 9. An information processing method in which one or more computers acquire vibration data measured using optical fiber laid in the monitored area, detect the bringing of an object into the monitored area based on the vibration data, and perform warning processing when the bringing of the object into the monitored area is detected. 10. A program that causes a computer to function as: acquisition means for acquiring vibration data measured using optical fiber laid in the monitored area, detection means for detecting the bringing of an object into the monitored area based on the vibration data, and warning means for performing warning processing when the bringing of the object into the monitored area is detected.

[0181] Some or all of Supplements 2 to 8 that are dependent on the information processing device of Supplement 1 described above may also be dependent on the information processing method of Supplement 9 and the program of Supplement 10 in the same dependent relationship as Supplement 1 and Supplements 2 to 8. Furthermore, within the scope of each of the above-mentioned embodiments, some or all of the configurations described as Supplements can be realized in various hardware, software, various recording means for recording software, or systems.

[0182] This application claims priority based on Japanese Patent Application No. 2024-114693, filed July 18, 2024, the disclosure of which is incorporated herein by reference in its entirety.

[0183] REFERENCE SIGNS LIST 1 Optical fiber 2 Monitoring area camera 3 Observation area camera 10 Information processing device 11 Acquisition unit 12 Detection unit 13 Warning unit 14 Adjustment unit 1A Processor 2A Memory 3A Input / output I / F 4A Peripheral circuit 5A Bus

Claims

1. An information processing device having: an acquisition means for acquiring vibration data measured using optical fiber laid in a monitored area; a detection means for detecting the bringing of an object into the monitored area based on the vibration data; and a warning means for performing a warning process when the bringing of the object into the monitored area is detected.

2. The information processing device described in claim 1, wherein the detection means: detects vibration-generating objects within the monitored area based on the vibration data and estimates the weight of the vibration-generating objects; detects possessions carried by a person from a monitored area image taken of the monitored area and estimates the volume of the possessions; and detects the bringing of the object into the monitored area based on the weight of the vibration-generating object and the volume of the possessions.

3. The information processing device described in claim 2, wherein the detection means estimates the weight of the object contained in the belongings if the object is contained in the belongings based on the density of the object and the volume of the belongings, and detects the bringing of the object into the monitored area based on the estimated weight of the vibration-generating object and the estimated weight of the object contained in the belongings.

4. An information processing device as described in any one of claims 1 to 3, wherein the detection means detects the bringing of the object into the monitoring area based on the difference between the vibration data before a person leaves the monitoring area and the vibration data after the person who has left the monitoring area returns to the monitoring area.

5. The information processing device described in claim 4, wherein the detection means identifies a first timing at which the person is detected before leaving the monitoring area and a second timing at which the person is detected after leaving the monitoring area and returning to the monitoring area based on a monitoring area image taken of the monitoring area, and detects the person bringing the object into the monitoring area based on the difference between the vibration data at the first timing and the vibration data at the second timing.

6. The information processing device described in claim 5, wherein the detection means estimates the amount of change in volume between the person's belongings detected from the surveillance area image at the first timing and the belongings detected from the surveillance area image at the second timing, estimates the amount of change in weight based on the difference between the vibration data at the first timing and the vibration data at the second timing, and detects the bringing of the object into the surveillance area based on the amount of change in volume of the belongings and the amount of change in weight.

7. The information processing device described in claim 6, wherein the detection means estimates the weight of the object contained in the possession when the object is contained in the possession based on the density of the object and the change in volume of the possession, and detects the bringing of the object into the monitored area based on the estimated weight of the object contained in the possession and the change in weight.

8. An information processing device as claimed in any one of claims 2, 3, 5, 6 and 7, wherein the monitoring area is an area passed through before entering an observation area where the object is present, and the detection means detects the object from an observation area image taken of the observation area, and has an adjustment means for adjusting the shooting position of a monitoring area camera that photographs the monitoring area based on the position of the object detected in the observation area image.

9. An information processing method in which one or more computers acquire vibration data measured using optical fiber laid in a monitored area, detect the bringing of an object into the monitored area based on the vibration data, and perform a warning process if the bringing of the object into the monitored area is detected.

10. The information processing method described in claim 9, wherein the one or more computers: detect vibration-generating objects within the monitored area based on the vibration data and estimate the weight of the vibration-generating objects; detect possessions carried by a person from monitored area images taken of the monitored area and estimate the volume of the possessions; and detect the bringing of the object into the monitored area based on the weight of the vibration-generating object and the volume of the possessions.

11. The information processing method described in claim 10, wherein the one or more computers estimate the weight of the object contained in the belongings if the object is contained in the belongings based on the density of the object and the volume of the belongings, and detect the bringing of the object into the monitored area based on the estimated weight of the vibration-generating object and the estimated weight of the object contained in the belongings.

12. An information processing method described in any one of claims 9 to 11, wherein the one or more computers detect the bringing of the object into the monitored area based on the difference between the vibration data before a person leaves the monitored area and the vibration data after the person who has left the monitored area returns to the monitored area.

13. The information processing method described in claim 12, wherein the one or more computers identify, based on a surveillance area image taken of the surveillance area, a first timing at which the person is detected before leaving the surveillance area and a second timing at which the person is detected returning to the surveillance area after leaving the surveillance area, and detect the person bringing the object into the surveillance area based on the difference between the vibration data at the first timing and the vibration data at the second timing.

14. The information processing method described in claim 13, wherein the one or more computers estimate the amount of change in volume between the person's belongings detected from the surveillance area image at the first timing and the belongings detected from the surveillance area image at the second timing, estimate the amount of change in weight based on the difference between the vibration data at the first timing and the vibration data at the second timing, and detect the bringing of the object into the surveillance area based on the amount of change in volume of the belongings and the amount of change in weight.

15. A recording medium having recorded thereon a program that causes a computer to function as: an acquisition means for acquiring vibration data measured using optical fiber laid in a monitored area; a detection means for detecting the bringing of an object into the monitored area based on the vibration data; and a warning means for performing a warning process when the bringing of the object into the monitored area is detected.

16. The recording medium described in claim 15, wherein the detection means: detects a vibration-generating object within the monitored area based on the vibration data and estimates the weight of the vibration-generating object; detects possessions carried by a person from a monitored area image taken of the monitored area and estimates the volume of the possessions; and detects the bringing of the object into the monitored area based on the weight of the vibration-generating object and the volume of the possessions.

17. The recording medium described in claim 16, wherein the detection means estimates the weight of the object contained in the belongings if the object is contained in the belongings based on the density of the object and the volume of the belongings, and detects the bringing of the object into the monitored area based on the estimated weight of the vibration-generating object and the estimated weight of the object contained in the belongings.

18. A recording medium described in any one of claims 15 to 17, wherein the detection means detects the bringing of the object into the monitored area based on the difference between the vibration data before the person leaves the monitored area and the vibration data after the person who left the monitored area returns to the monitored area.

19. The recording medium described in claim 18, wherein the detection means identifies a first timing at which the person is detected before leaving the monitored area and a second timing at which the person is detected after leaving the monitored area and returning to the monitored area based on a monitored area image taken of the monitored area, and detects the person bringing the object into the monitored area based on the difference between the vibration data at the first timing and the vibration data at the second timing.

20. The recording medium described in claim 19, wherein the detection means estimates the amount of change in volume between the person's belongings detected from the surveillance area image at the first timing and the belongings detected from the surveillance area image at the second timing, estimates the amount of change in weight based on the difference between the vibration data at the first timing and the vibration data at the second timing, and detects the bringing of the object into the surveillance area based on the amount of change in volume of the belongings and the amount of change in weight.

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