Information processing device, information processing system, information processing method, and recording medium

Abstract

This information processing device comprises a suspicious person determination unit and a notification control unit. The suspicious person determination unit determines, by using presence-in-residence information indicating the presence-in-residence state of a resident of a residence, whether a person that has been detected by using sensor information obtained from a sensor for monitoring the site of the residence is a suspicious person. If it has been determined that the detected person is a suspicious person, the notification control unit causes a notification unit to issue a notification to at least one of the resident and the person determined to be the suspicious person.

Description

Information processing device, information processing system, information processing method, and recording medium

[0001] The present invention relates to an information processing device, an information processing system, an information processing method, and a recording medium.

[0002] For example, a security system described in Patent Document 1 includes an on-board monitoring device, a security camera, and a monitoring server mounted on a vehicle, and detects suspicious individuals in and around a monitored home. When the vehicle is a delivery truck, the vehicle constantly captures and monitors the surrounding area while delivering packages, and also captures and monitors the surrounding area when traveling within a predetermined range from the home. When the on-board monitoring device determines that a person captured by the on-board camera is a suspicious individual and is absent through a home presence determination process, it notifies the monitoring server of the suspicious individual detection information. Upon receiving the notification, the monitoring server transmits the suspicious individual detection information to a security guard mobile terminal and the owner's mobile terminal.

[0003] The information used to determine whether a person is at home as described in Patent Document 1 includes information about the amount of electricity used by the home, information about the security status of the home, and information about the home's absence history. The information about electricity used is held by a company that supplies electricity to the home. The information about the security status is held by a company that provides security services to the home. The information about the absence history is held by a delivery company that delivers packages to the home.

[0004] Japanese Patent Application Laid-Open No. 2018-190199

[0005] However, the possibility of a suspicious individual appearing is generally related to the presence or absence of a person at home. In the technology described in Patent Document 1, the result of the presence or absence determination process is used to determine whether or not to notify the monitoring server of suspicious individual detection information. As a result, the suspicious individual may not be detected appropriately, which may jeopardize the safety of the home.

[0006] One of the objectives of the present disclosure is to improve safety in homes.

[0007] The information processing device of the present disclosure includes a suspicious person determination means that determines whether a person detected using sensor information obtained from a sensor monitoring the grounds of a house is a suspicious person using presence information that indicates whether a resident of the house is at home, and an alarm control means that, when the detected person is determined to be the suspicious person, causes an alarm unit to issue an alarm to at least one of the person determined to be the suspicious person and the resident.

[0008] The information processing system of the present disclosure includes a sensor that monitors the premises of a house; an analysis means that detects a person present on the premises using sensor information generated by the sensor; a suspicious person determination means that determines whether the detected person is a suspicious person using presence information that indicates whether a resident of the house is present; and an alarm control means that, when the detected person is determined to be the suspicious person, causes an alarm unit to issue an alarm to at least one of the person determined to be the suspicious person and the resident.

[0009] The information processing method disclosed herein involves one or more computers determining whether a person detected using sensor information obtained from a sensor monitoring the grounds of a house is a suspicious person using presence information indicating whether a resident of the house is at home, and if the detected person is determined to be a suspicious person, causing an alarm unit to issue an alarm to at least one of the person determined to be a suspicious person and the resident.

[0010] The recording medium in the present disclosure has recorded thereon a program for causing one or more computers to determine whether a person detected using sensor information obtained from a sensor monitoring the grounds of a house is a suspicious person using presence information indicating whether the resident of the house is at home, and if the detected person is determined to be the suspicious person, to cause an alarm unit to issue an alarm to at least one of the person determined to be the suspicious person or the resident.

[0011] According to the present disclosure, it is possible to improve safety in homes.

[0012] 1 is a block diagram showing a configuration example of a first information processing system according to the present disclosure. FIG. 2 is a block diagram showing a configuration example of a first information processing device according to the present disclosure. FIG. 3 is a flowchart showing a processing operation example of the first information processing device according to the present disclosure. FIG. 4 is a block diagram showing a device configuration example of the first information processing system according to the present disclosure. FIG. 5 is a block diagram showing a configuration example of a first analysis device according to the present disclosure. FIG. 6 is a diagram showing a processing operation example of the first information processing system according to the present disclosure. FIG. 7 is a block diagram showing a physical configuration example of a first information processing device according to the present disclosure. FIG. 8 is a block diagram showing a device configuration example of a second information processing system according to the present disclosure. FIG. 9 is a block diagram showing a configuration example of a second information processing device according to the present disclosure. FIG. 10 is a diagram showing a processing operation example of the second information processing system according to the present disclosure. FIG. 11 is a block diagram showing a device configuration example of a third information processing system according to the present disclosure. FIG. 12 is a block diagram showing a configuration example of a third information processing device according to the present disclosure. FIG. 13 is a diagram showing a processing operation example of the third information processing system according to the present disclosure.

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings, similar components are designated by similar reference numerals, and descriptions thereof will be omitted as appropriate. In addition, in this disclosure, the drawings relate to one or more embodiments.

[0014] First Embodiment (Overview) For example, Patent Document 2 (Utility Model Registration No. 3174605) describes a security device that includes a plurality of motion detection sensors, a device main body, and an alarm unit.

[0015] The plurality of motion detection sensors described in Patent Document 2 transmits sensor signals.

[0016] The device main body described in Patent Document 2 includes a signal receiving circuit, a detection order recognition circuit, a detection time measurement circuit, and a suspicious person determination circuit. The signal receiving circuit is connected to each motion detection sensor and identifies and receives a sensor signal from the motion detection sensor that first transmitted the signal. The detection order recognition circuit recognizes the detection order of the multiple motion detection sensors in chronological order. The detection time measurement circuit measures the detection time of the motion detection sensors. The suspicious person determination circuit determines whether a motion is due to a suspicious person's movement based on the detection order, etc.

[0017] The notification unit described in Patent Document 2 is connected to the device main body and notifies the presence of a suspicious person when the suspicious person determination circuit determines that the movement is suspicious.

[0018] The security device described in Patent Document 2, like the security system described in Patent Document 1, does not take into consideration that the possibility of a suspicious person appearing is related to whether or not a person is at home in the home. As a result, it is not possible to properly detect a suspicious person, which could jeopardize the safety of the home.

[0019] (Configuration Example of Information Processing System S1) As shown in FIG. 1, the information processing system S1 includes a sensor 10, an analysis unit 52, a suspicious person determination unit 130, and a notification control unit 140.

[0020] The sensor 10 monitors the residential premises.

[0021] The analysis unit 52 uses the sensor information generated by the sensor 10 to detect people present on the premises.

[0022] The suspicious person determination unit 130 determines whether the detected person is a suspicious person by using presence information indicating whether the resident is at home in the house.

[0023] When the detected person is determined to be a suspicious person, the notification control unit 140 causes the notification unit to issue a notification to at least one of the person determined to be a suspicious person and the resident.

[0024] This information processing system S1 can appropriately detect suspicious individuals based on the likelihood of them appearing in relation to the presence of resident(s) at home, thereby improving safety in the home.

[0025] (Configuration Example of Information Processing Device 100) As shown in FIG. 2, the information processing device 100 includes a suspicious person determination unit 130 and a notification control unit 140.

[0026] The suspicious person determination unit 130 determines whether a person detected using sensor information obtained from a sensor 10 monitoring the premises of a house is a suspicious person by using presence information indicating the presence status of the resident of the house.

[0027] When the detected person is determined to be a suspicious person, the notification control unit 140 causes the notification unit to issue a notification to at least one of the person determined to be a suspicious person and the resident.

[0028] The information processing device 100 can appropriately detect suspicious individuals based on the likelihood of the individual appearing in relation to the presence or absence of resident(s) at home in a home, thereby improving safety in the home.

[0029] (Example of Processing Operation of Information Processing Device 100) The information processing device 100 executes information processing as shown in FIG.

[0030] The suspicious person determination unit 130 determines whether a person detected using sensor information obtained from a sensor 10 monitoring the premises of a house is a suspicious person using presence information that indicates the presence status of resident(s) at home in the house (step S130).

[0031] If the detected person is determined to be a suspicious person, the notification control unit 140 causes the notification unit to issue a notification to at least one of the person determined to be a suspicious person and the resident (step S140).

[0032] This information processing makes it possible to appropriately detect suspicious individuals based on the likelihood of them appearing in relation to the occupants' presence at home, thereby improving safety in the home.

[0033] (Device Configuration Example of Information Processing System S1) The information processing system S1 includes the above-described sensor 10, an analysis device 50, and the above-described information processing device 100, as shown in FIG. 4, for example.

[0034] The sensor 10, the analysis device 50, and the information processing device 100 are connected to each other via a communication network NT that is configured, for example, by wire, wireless, or a combination of these, and transmit and receive various information to and from each other via the communication network NT.

[0035] The sensor 10 and the analysis device 50, and the analysis device 50 and the information processing device 100 may be connected via different communication networks.

[0036] (Configuration Example of Analysis Device 50) As shown in FIG. 5, the analysis device 50 includes a sensor information acquisition unit 51 and the above-described analysis unit 52.

[0037] The sensor information acquisition unit 51 acquires sensor information from the sensor 10 that monitors the premises of the house.

[0038] (Example of Processing Operation of Information Processing System S1) The information processing system S1 executes information processing as shown in FIG. 6, for example.

[0039] The sensor 10 monitors the premises of the house and generates sensor information (step S10).

[0040] The sensor information acquisition unit 51 acquires sensor information from the sensor 10 that monitors the premises of the house (step S51).

[0041] The analysis unit 52 uses the sensor information to detect people present on the premises (step S52).

[0042] Steps S130 and S140 are executed.

[0043] (Regarding the sensor 10) The sensor 10 is a sensor for monitoring the surroundings of a house, i.e., the house's grounds. There may be one or more sensors 10. The sensor 10 may include, for example, at least one of a house sensor installed in association with the house and an on-board sensor installed in a car parked on the grounds.

[0044] That is, the sensor 10 may include only one or more residential sensors installed in association with the residence, or only one or more vehicle sensors installed in a vehicle parked on the lot, or the sensor 10 may be multiple and include one or more residential sensors installed in association with the residence and one or more vehicle sensors installed in a vehicle parked on the lot.

[0045] The residential sensor may include, for example, at least one of a camera installed on the premises, a residential object presence detection sensor, etc. The vehicle-mounted sensor may include, for example, at least one of an in-vehicle camera, an in-vehicle LiDAR (Light Detection and Ranging) sensor, an in-vehicle object presence detection sensor, etc.

[0046] The camera installed on the premises may be installed, for example, at a gate, entrance, fence, wall, pole on the premises, etc. The camera installed on the premises may include a camera installed inside a house. The camera installed on the premises may capture an image of a predetermined capture area according to its installation location and generate sensor information including the captured image.

[0047] The on-board camera may be mounted on a vehicle to capture, for example, an image of the exterior or interior of the vehicle. The on-board camera may capture, for example, an image of a predetermined area outside or inside the vehicle and generate sensor information including the captured image.

[0048] An on-board LiDAR sensor is a LiDAR sensor mounted on an automobile. The LiDAR sensor is a sensor used for LiDAR. LiDAR is a technology that irradiates a laser beam and measures the distance to an object, the shape of the object, etc., using the reflected light. The on-board LiDAR sensor may include, for example, a laser irradiator that irradiates the laser beam and an optical receiver that receives the reflected light. The on-board LiDAR sensor may, for example, irradiate a laser beam to a predetermined area outside the automobile and generate sensor information indicating the reflected light.

[0049] The sensor 10 may include a LiDAR sensor installed on the site.

[0050] The residential object presence detection sensor is, for example, a sensor that detects an object that exists within a predetermined distance. The vehicle-mounted object presence detection sensor is, for example, a sensor that detects an object that exists within a predetermined distance from the vehicle. The vehicle-mounted object presence detection sensor may be, for example, a sensor installed to prevent the vehicle from colliding.

[0051] The object presence detection sensor emits, for example, sound waves, electromagnetic waves, etc., and generates sensor information indicating the reflected waves. The sound waves, electromagnetic waves, etc. used by the object presence detection sensor may include at least one of an ultrasonic sensor, an infrared sensor, and a millimeter wave sensor. The object presence detection sensor may include, for example, an oscillator that emits the sound waves, electromagnetic waves, etc., and a receiver that receives the reflected waves.

[0052] (Sensor Information Acquisition Unit 51) The sensor information acquisition unit 51 acquires, for example, sensor information generated by each of one or more sensors 10. The sensor information acquisition unit 51 acquires the sensor information via, for example, the communication network NT.

[0053] (Analysis Unit 52) ​​The analysis unit 52 has an analysis function for performing analysis processing on one or more pieces of sensor information generated by each of one or more sensors 10.

[0054] For example, the analysis unit 52 generates an analysis result using one or more pieces of sensor information acquired by the sensor information acquisition unit 51. The analysis unit 52 transmits the generated analysis result to the information processing device 100, for example, via the communication network NT.

[0055] The analysis unit 52 uses its analysis function to detect objects present on the premises of the house and generate analysis results relating to the objects.

[0056] The object includes a person and an object, such as a car, but is not limited to this and may include a bicycle, for example.

[0057] The analysis result may include, for example, one or more of the time when the object was detected, the position of the object, the moving speed of the object, the moving path of the object, the posture of the object, and the attributes of the object.

[0058] The attributes of the moving object may include, for example, the type of the moving object, such as a person, a car, etc. If the moving object is a person, the attributes of the moving object may include, for example, one or more of height, build, clothing, gender, age group, etc. If the moving object is a car, the attributes of the moving object may include, for example, one or more of model, size, color, etc.

[0059] A detailed example of a function for generating such an analysis result will be described below. Note that the information included in the analysis result is not limited to the above example.

[0060] The analysis unit 52 may have an analysis function according to the type of sensor information, such as the above-mentioned camera, LiDAR sensor, or object presence detection sensor.

[0061] In more detail, for example, the analysis unit 52 may analyze the sensor information generated by each of one or more sensors 10 using an analysis function according to the type of the sensor information. When there are multiple pieces of sensor information, the analysis unit 52 may integrate the analysis results (individual analysis results) of each piece of sensor information to generate an analysis result (overall analysis result) that analyzes the multiple pieces of sensor information as a whole. When simply referring to the "analysis result," this means the overall analysis result.

[0062] When integrating individual analysis results, for example, objects detected from each sensor information (i.e., objects included in the individual analysis results) may be treated as individual objects and combined to generate an analysis result. Furthermore, when integrating individual analysis results, if objects included in the individual analysis results are within a predetermined distance, such nearby objects may be treated as a single object to generate an analysis result. In this case, the position, detection time, etc. of the nearby object may be, for example, an average of the positions, times, etc. included in the individual analysis results, or any one of the positions, times, etc. included in the individual analysis results.

[0063] The method for integrating the individual analysis results is not limited to the method described here, and any general technique may be applied.

[0064] In general, the accuracy of the individual analysis results may vary depending on the environment, such as the brightness around the sensor 10. Therefore, by integrating the individual analysis results, it is possible to obtain more accurate analysis results.

[0065] An example of a method for generating individual analysis results for each type of sensor information will be described below.

[0066] (Example of a method for generating individual analysis results of sensor information generated by a camera) For example, the analysis unit 52 has one or more image analysis functions for analyzing images such as videos and still images. Each of the image analysis functions may extract an image feature vector and perform analysis using, for example, a machine learning model that has been trained to perform analysis according to the function. The machine learning model may be configured using, for example, a neural network. Note that, while an example of analyzing an image using a machine learning model will be described here, image analysis is not limited to this, and general image processing techniques such as pattern matching may also be used.

[0067] The image analysis functions provided by the analysis unit 52 include, for example, one or more of: (1) object detection function, (2) face analysis function, (3) human figure analysis function, (4) posture analysis function, (5) behavior analysis function, (6) appearance attribute analysis function, (7) gradient feature analysis function, (8) color feature analysis function, and (9) movement line analysis function.

[0068] (1) The object detection function detects an object from an image. The object detection function can also determine the position of an object within an image. For example, YOLO (You Only Look Once) is a model that can be applied to the object detection process.

[0069] (2) The face analysis function detects human faces from images, extracts feature vectors of the detected faces, and classifies the detected faces. The face analysis function can also determine the position of the face in the image, as well as the direction of the face and gaze. The face analysis function can also determine the identity of people detected from different images based on the similarity between the facial features of people detected from different images.

[0070] (3) The human morphology analysis function extracts the physical characteristics of people included in an image (for example, values ​​indicating overall characteristics such as whether they are fat or thin, their height, and their clothing), and classifies (classifies) people included in an image. The human morphology analysis function can also identify the position of a person within an image. The human morphology analysis function can also determine the identity of people included in different images based on the physical characteristics of the people included in different images.

[0071] (4) The posture analysis function detects the joint points of a person from an image and creates a stick figure model by connecting the joint points. The posture analysis function then uses the information from the stick figure model to estimate the posture of the person, extract a feature vector of the estimated posture (posture feature vector), and classify (classify) the people included in the image. The posture analysis function can also determine the identity of people included in different images based on the posture feature vectors of the people included in different images.

[0072] For example, the posture analysis function estimates a person's posture, such as standing, crouching, or bending, from an image and extracts a posture feature vector indicating the posture of each person.Furthermore, for example, the posture analysis function can estimate the posture of an object detected using an object detection function or the like from an image and extract a posture feature vector indicating that posture.

[0073] (5) The behavior analysis process can estimate a person's movements using information about the stick figure model, changes in posture, and the like, extract a feature vector of the person's movements (motion feature vector), and classify (classify) people included in the image. The behavior analysis process can also estimate a person's height and identify the person's position in the image using information about the stick figure model. The behavior analysis process can estimate, for example, a person's movements, such as changes or transitions in posture and movement (changes or transitions in position), from the image, and extract a motion feature vector related to the person's movements.

[0074] (6) The appearance attribute analysis function can recognize appearance attributes associated with a person. The appearance attribute analysis function extracts feature vectors (appearance attribute feature vectors) related to the recognized appearance attributes and classifies (classifies) people included in an image. Appearance attributes are attributes of a person's appearance. Appearance attributes include, for example, one or more of age group, gender, type and color of clothing, type and color of shoes, hairstyle, whether or not a hat is worn, whether or not a tie is worn, whether or not glasses are worn, whether or not an umbrella is carried, and whether or not an umbrella is being used.

[0075] (7) The gradient feature analysis function extracts gradient feature quantities (gradient feature quantities) in an image. For gradient feature detection processing, techniques such as SIFT, SURF, RIFF, ORB, BRISK, CARD, and HOG can be applied.

[0076] SIFT is an abbreviation for Scale-Invariant Feature Transform. SURF is an abbreviation for Speeded-Up Robust Features. RIFF is an abbreviation for Rotation-Invariant Fast Feature. BRIEF is an abbreviation for Binary Robust Independent Elementary Features. ORB is an abbreviation for Oriented FAST and Rotated BRIEF. BRISK is an abbreviation for Binary Robust Invariant Scalable Keypoints. CARD is an abbreviation for Compact And Real-time Descriptors. HOG is an abbreviation for Histograms of Oriented Gradients.

[0077] (8) The color feature analysis function can detect an object from an image, extract a color feature vector of the detected object, and classify (classify) the detected object.

[0078] The color feature amount is, for example, a color histogram. The color feature analysis function can, for example, detect people and objects included in an image. Furthermore, for example, the color feature analysis function can classify each of the people and objects into classes.

[0079] (9) The flow line analysis function can determine the flow line (trajectory of movement) of a person included in a video, for example, using the results of the identity determination in any of the analysis functions (2) to (6) described above. In more detail, for example, by connecting people determined to be the same across different images in a time series, the flow line of that person can be determined. Note that, in cases where video footage is acquired using multiple in-vehicle cameras or the like that capture different shooting areas, the flow line analysis function can also determine the flow line across multiple videos captured in different shooting areas.

[0080] The image feature vector includes, for example, the object detection result of the object detection function, a face feature vector, a human body feature vector, a posture feature vector, a movement feature vector, an appearance attribute feature vector, a gradient feature vector, a color feature vector, and a movement line.

[0081] Each of the analysis functions (1) to (9) may appropriately use the results of analysis performed by other analysis functions.

[0082] (Example of a method for generating individual analysis results of sensor information generated by a LiDAR sensor) The analysis unit 52 generates point cloud information using the sensor information generated by the LiDAR sensor. A general technique may be used to generate the point cloud information. The analysis unit 52 uses the point cloud information to generate individual analysis information including at least one of the position, shape, and detection time of an object.

[0083] (Example of a method for generating individual analysis results of sensor information generated by an object presence detection sensor) The analysis unit 52 may use, for example, the sensor information generated by the object presence detection sensor to generate individual analysis information including at least one of the object position, the time of detection, etc. A general technique may be used as a technique for generating such individual analysis information.

[0084] (Regarding the suspicious person determination unit 130) As described above, the suspicious person determination unit 130 determines whether a person detected using sensor information obtained from the sensor 10 monitoring the premises of the house is a suspicious person by using presence information that indicates the presence status of the resident of the house.

[0085] For example, the suspicious person determination unit 130 determines whether a person included in the analysis result of the analysis unit 52 is a suspicious person by using presence information indicating whether the resident is at home in the house.

[0086] The presence-at-home information may be, for example, information indicating whether at least one resident is present at the home. The presence-at-home information may be, for example, information indicating whether each of multiple residents is present at the home. In this case, the presence-at-home information may be associated with the attributes (e.g., age, gender, etc.) of each of the multiple residents.

[0087] For example, to determine whether a person is suspicious, predetermined determination conditions regarding the presence-at-home status and the presence-at-home information may be used.

[0088] The judgment condition may be, for example, a condition in which a suspiciousness level indicating the degree to which a person is suspicious is associated with a threshold value according to the presence / absence status. In this case, for example, the suspicious person judgment unit 130 may judge a person whose suspiciousness level is equal to or greater than the threshold value according to the presence / absence status to be a suspicious person.

[0089] (Regarding suspiciousness level) The suspiciousness level may be determined, for example, in association with an event related to a person. In this case, the suspicious person determination unit 130 may determine the suspiciousness level of a detected person using the suspiciousness level determined in advance in association with an event related to a person.

[0090] An event may be composed of one or more elements (suspicious elements). The suspicious elements may include, for example, one or more of: (A) elements related to the person's behavior; (B) elements related to the time period when the person was detected; (C) elements related to the person's attributes; (D) elements related to the situation around the person.

[0091] That is, the suspicious person determination unit 130 may further use at least one of, for example, the movement of the detected person, the time period when the person was detected, the attributes of the detected person, and the circumstances surrounding the detected person to determine whether the detected person is a suspicious person.

[0092] (A) Elements related to a person's movements may include, for example, one or more elements related to (A-1) movement speed, (A-2) position, (A-3) behavior, (A-4) movement path, (A-5) frequency, etc.

[0093] (A-1) The elements belonging to the moving speed include, for example, at least one of stopping, a low speed equal to or lower than a predetermined speed, and the like.

[0094] (A-2) The element belonging to the location is, for example, at least one of being within a predetermined distance from a reference location, etc. The reference location is, for example, a place that is likely to be connected to a danger in a house, such as a gate, a window, a back door, a fence, etc.

[0095] (A-3) Elements belonging to behavior include, for example, at least one of the following: a crouching or half-squatting posture; a face or gaze directed toward a house, on the premises, or indoors; not pressing the intercom; and no contact with residents.

[0096] (A-4) The elements belonging to the movement route are, for example, at least one of the trajectory of movement.

[0097] (A-5) The frequency element is, for example, at least one of a predetermined action being performed at a frequency equal to or greater than a predetermined frequency within a predetermined period of time. The predetermined action here may be defined using, for example, one or more of the elements exemplified in (A-1) to (A-4). The predetermined period may be determined, for example, according to the period during which the associated predetermined action is generally repeated in suspicious behavior.

[0098] (B) The element relating to the time period in which a person was detected is, for example, at least one of nighttime, early morning, daytime, etc. Nighttime is, for example, 10 PM to 12 PM and 12 PM to 3 AM. Early morning is, for example, 3 AM to 5 AM. Daytime is, for example, a time period when residents are often not at home (for example, 10 AM to 3 PM). Note that examples of time periods are not limited to those given here.

[0099] (C) The element relating to the person's attributes is, for example, at least one of the following: the person's height is above a predetermined value; the person is of a predetermined gender, either male or female; the person rides a motorbike or bicycle; and the like.

[0100] (D) The element relating to the situation around the person is, for example, at least one of the following: the brightness of the premises is dark, below a threshold, the weather is cloudy or rainy, and the like.

[0101] Using these suspicious elements, events of a person that are generally likely to be suspicious can be defined, such as loitering, peeking, suspicious posture, suspicious movement, etc. Then, a degree of suspiciousness according to the possibility that the person performing the event is suspicious can be defined in association with the event.

[0102] Prowling may be defined as, for example, moving slowly for a predetermined period of time or more, moving slowly a predetermined number of times or more, moving slowly a predetermined number of times or more within a predetermined period of time, etc. Prowling may also be defined as, for example, approaching a residence more frequently than a predetermined number of times without contacting the resident or without pressing the intercom.

[0103] Peering may be defined as, for example, walking or walking slowly while looking at a house within a predetermined distance from the property, or turning one's face into the house from a window or the like within a predetermined distance.

[0104] A suspicious posture may be defined as, for example, crouching, crouching under a vehicle, etc.

[0105] Suspicious movement may be defined as, for example, movement from the gate to the front door via a route that is not the shortest route from the gate to the front door.

[0106] (Regarding Thresholds According to Presence Status) The thresholds according to the presence status may be determined in advance. In this case, the suspicious person determination unit 130 may use the presence status information to determine the thresholds according to the presence status indicated by the presence status information.

[0107] For example, the threshold value according to the presence-at-home status may be set lower when no resident is present at the home than when at least one resident is present at the home. This allows the conditions for detecting a suspicious individual to be relaxed when no resident is present at the home. Therefore, it is possible to appropriately detect a suspicious individual by taking advantage of the characteristic of suspicious individuals that they generally appear when no resident is present.

[0108] For example, the threshold value according to the presence-at-home status may be set lower when only elderly people or children are present in the home than when other residents are present in the home. This allows the conditions for detecting suspicious individuals to be relaxed when other residents are absent from the home. Therefore, suspicious individuals can be appropriately detected by taking advantage of the characteristic of suspicious individuals that they generally appear when only elderly people or children are present in the home.

[0109] The suspiciousness level, thresholds according to the presence-at-home status, and judgment conditions are not limited to the examples given here.

[0110] (Alarm control unit 140) As described above, when a detected person is determined to be a suspicious person, the alarm control unit 140 causes the alarm unit to issue an alarm to at least one of the person determined to be a suspicious person and the resident.

[0111] The notification unit that issues a warning to a person determined to be a suspicious person is, for example, a light, a horn, etc. that is mounted on the vehicle. The notification unit that issues a warning to a person determined to be a suspicious person is, for example, a light, a buzzer, etc. that is installed on the premises.

[0112] The notification control unit 140 may turn on or flash at least one of these lights, or sound a sound from a horn, buzzer, etc. This notifies a person determined to be a suspicious person that they have been detected, thereby threatening them and reducing the possibility of danger to the house.

[0113] The notification unit that notifies the resident is, for example, a mobile terminal used by the resident. The mobile terminal is, for example, a smartphone, a tablet terminal, or the like.

[0114] The notification control unit 140 may, for example, send information indicating that a suspicious person has been detected to the mobile terminal, causing the mobile terminal to display or ring, etc. This allows the resident to know that a suspicious person has been detected and to take measures to reduce the possibility of danger to the house.

[0115] (Example of Physical Configuration of Information Processing Apparatus 100) As shown in FIG. 7, the information processing apparatus 100 physically includes a bus 1010, a processor 1020, a memory 1030, a storage device 1040, a network interface 1050, and a user interface 1060.

[0116] The bus 1010 is a data transmission path for transmitting and receiving data among the processor 1020, memory 1030, storage device 1040, network interface 1050, input interface 1060, and output interface 1070. However, the method of connecting the processor 1020 and the like to each other is not limited to bus connection.

[0117] The processor 1020 is implemented as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit).

[0118] The memory 1030 is a main storage device realized by a RAM (Random Access Memory) or the like.

[0119] The storage device 1040 is an auxiliary storage device realized by a hard disk drive (HDD), a solid state drive (SSD), a memory card, a read-only memory (ROM), or the like. The storage device 1040 stores program modules for realizing the functions of the device that includes the storage device 1040. The processor 1020 loads each of these program modules into the memory 1030 and executes them to realize the function corresponding to that program module.

[0120] The network interface 1050 is an interface for connecting a device equipped with it to a communication network.

[0121] The input interface 1060 is an interface for the user to input information, and is configured from, for example, a touch panel, a keyboard, a mouse, and the like.

[0122] The output interface 1070 is an interface for presenting information to the user, and is configured, for example, by a liquid crystal panel, an organic EL (Electro-Luminescence) panel, or the like.

[0123] In this way, the functions of the information processing device 100 can be realized by the physical components cooperating to execute a software program. Therefore, the present invention may be realized as a software program or as a non-transitory storage medium on which the program is recorded.

[0124] The analysis device 50 may be physically configured in the same manner as the information processing device 100, for example.

[0125] The functions of the analysis device 50 and the information processing device 100 as a whole are not limited to those described above, and may be provided in one or more devices. For example, the information processing device 100 may have the functions of the analysis device 50 and be physically integrated with the analysis device 50.

[0126] (Operations and Effects) As described above, according to this embodiment, the information processing device 100 includes the suspicious person determination unit 130 and the notification control unit 140 .

[0127] The suspicious person determination unit 130 determines whether a person detected using sensor information obtained from a sensor 10 monitoring the premises of a house is a suspicious person by using presence information indicating the presence status of the resident of the house.

[0128] When the detected person is determined to be a suspicious person, the notification control unit 140 causes the notification unit to issue a notification to at least one of the person determined to be a suspicious person and the resident.

[0129] This allows for appropriate detection of suspicious individuals based on the likelihood of them appearing in relation to the presence of resident(s) at home, thereby improving safety in the home.

[0130] According to this embodiment, there are a plurality of sensors 10. The plurality of sensors 10 includes one or more residential sensors installed in association with a residence and one or more vehicle sensors installed in a vehicle parked on the premises.

[0131] This allows the area monitored by the sensor 10 to be wider than when the sensor 10 is only a home sensor or an in-vehicle sensor, thereby improving safety in the home.

[0132] According to this embodiment, the suspicious person determination unit 130 further uses at least one of the movement of the detected person, the time period when the person was detected, the attributes of the detected person, and the circumstances surrounding the detected person to determine whether the detected person is a suspicious person.

[0133] This allows for the appropriate detection of suspicious individuals, thereby improving the safety of homes.

[0134] (Variation 1) In the first embodiment, an example in which one analysis device 50 is used has been described, but the information processing system may include multiple analysis devices. For example, if the information processing system includes multiple sensors 10, each of the multiple analysis devices may be associated with a sensor group consisting of one or more sensors 10. In this case, each analysis device may analyze sensor information generated by one or more sensors 10 that make up the sensor group and integrate these individual analysis results as necessary. The suspicious person determination unit 130 may integrate the individual analysis results generated by each of the multiple analysis devices or the integrated individual analysis results to generate an analysis result.

[0135] [Embodiment 2] In this embodiment, an example of a method for generating presence-at-home information will be described. In this embodiment, for the sake of simplicity, descriptions that overlap with other embodiments will be omitted as appropriate.

[0136] (Device Configuration Example of Information Processing System S2) The information processing system S2 includes the sensor 10 and the analysis device 50 similar to those in the first embodiment, and an information processing device 200, as shown in FIG. 8, for example.

[0137] (Configuration Example of Information Processing Device 200) The information processing device 200 includes a suspicious person determination unit 130 and a notification control unit 140 similar to those in the first embodiment, and a presence-at-home information generation unit 250, as shown in FIG. 9, for example.

[0138] The presence-at-home information generating unit 250 generates presence-at-home information using at least one of the resident's location information and sensor information.

[0139] (Example of Processing Operation of Information Processing System S2) The information processing system S2 executes information processing as shown in FIG. 10, for example.

[0140] Steps S10, S51, and S52 are executed in the same manner as in the first embodiment.

[0141] The presence-at-home information generating unit 250 generates presence-at-home information using at least one of the resident's location information and sensor information (Step S250).

[0142] Steps S130 and S140 are executed in the same manner as in the first embodiment.

[0143] (Regarding the Presence Information Generating Unit 250) As described above, the presence information generating unit 250 generates presence information using at least one of the location information of the resident and the history information of the person detected using the sensor information.

[0144] The resident's location information may be obtained using, for example, a GPS (Global Positioning System) function provided in a mobile device used by the resident. In this case, the presence-presence information generation unit 250 may obtain location information indicating the resident's current location from, for example, the mobile device used by the resident. The presence-presence information generation unit 250 may then determine whether the resident is present at home based on, for example, preset location information of the home and the resident's current location, and generate presence-presence information based on the result of this determination.

[0145] The history information of a person detected using sensor information is information indicating the history of detection of the person using sensor information, such as a history of analysis results including the person detection results. This sensor information may be generated by, for example, a camera installed outside a house, and may include an image.

[0146] In this case, the presence information generation unit 250 may, for example, use the resident's history information among the people detected using sensor information to determine whether the resident has returned home or not, and generate presence information based on the results of this determination.

[0147] There are various methods for identifying a resident from among people detected using sensor information. For example, resident information for identifying a resident may be set in advance, and the presence-at-home information generation unit 250 may identify the resident using the resident information. If the sensor information includes an image, the resident information may be, for example, an image of the resident's face, a facial feature vector extracted from the image, or the like.

[0148] That is, the sensor information may include images generated by a camera. In this case, the presence-at-home information may be generated using history information of the resident detected using the images.

[0149] It should be noted that the sensor information used to generate the presence-at-home information is not limited to sensor information including images. For example, if a resident has a designated path through which to go out and return home on the premises, and sensor 10 is an object presence detection sensor that detects the presence or absence of an object in this path, the sensor information generated by the object presence detection sensor may be used to generate the presence-at-home information.

[0150] Furthermore, the presence-at-home information generating unit 250 may generate presence-at-home information using both the location information of the resident and history information of the person detected using sensor information.

[0151] (Actions and Effects) As described above, according to this embodiment, the information processing device 200 further includes a presence-at-home information generation unit 250 that generates presence-at-home information using at least one of the location information of the resident and the history information of the person detected using the sensor information.

[0152] This allows automatic generation of presence information that accurately indicates whether residents are at home, and this presence information can be used to appropriately detect suspicious individuals, thereby improving safety in homes.

[0153] According to this embodiment, the sensor information includes images, and the presence-at-home information is generated using history information of the resident detected using the images.

[0154] This allows automatic generation of presence information that accurately indicates whether residents are at home, and this presence information can be used to appropriately detect suspicious individuals, thereby improving safety in homes.

[0155] [Embodiment 3] In this embodiment, an example of a method for identifying non-suspicious individuals who are not suspicious individuals and using the identified non-suspicious individuals to generate presence-at-home information will be described. Non-suspicious individuals include residents. That is, this embodiment includes a description of an example of a method for identifying residents. Furthermore, this embodiment also describes an example of a method for detecting suspicious individuals using identified non-suspicious individuals.

[0156] (Device Configuration Example of Information Processing System S3) The information processing system S3 includes the sensor 10 and analysis device 50 similar to those in the first embodiment, and an information processing device 300, as shown in FIG. 11, for example.

[0157] (Configuration example of information processing device 300) As shown in FIG. 12, the information processing device 300 includes a suspicious person determination unit 330, a notification control unit 140 similar to that in the first embodiment, a presence information generation unit 350, and a non-suspicious person identification unit 360.

[0158] The non-suspicious individual identification unit 360 identifies a non-suspicious individual using history information of a person detected using sensor information.

[0159] The presence-at-home information generation unit 350 generates presence-at-home information for a resident identified as a non-suspicious person using history information of the resident detected using images. That is, the sensor information includes images, and the presence-at-home information is generated for a resident identified as a non-suspicious person using history information of the resident detected using the images.

[0160] The suspicious person determination unit 330 determines whether or not a person is a suspicious person, excluding non-suspicious people from among the detected people, using the presence-at-home information.

[0161] (Example of Processing Operation of Information Processing System S3) The information processing system S3 executes information processing as shown in FIG. 13, for example.

[0162] Steps S10, S51, and S52 are executed in the same manner as in the first embodiment.

[0163] The non-suspicious individual identification unit 360 identifies a non-suspicious individual using history information of a person detected using sensor information (step S360).

[0164] The presence-at-home information generation unit 350 generates presence-at-home information using historical information of the resident detected using the image. That is, the sensor information includes an image, and the presence-at-home information is generated using the historical information of the resident detected using the image (step S350).

[0165] The suspicious person determination unit 330 determines whether or not each person, excluding the non-suspicious person, is a suspicious person from among the detected people, using the presence-at-home information (step S330).

[0166] Step S140 is executed in the same manner as in the first embodiment.

[0167] (Regarding the non-suspicious person identification unit 360) As described above, the non-suspicious person identification unit 360 identifies a non-suspicious person using history information of a person detected using sensor information. A non-suspicious person is, for example, a resident, a delivery person, or another person who frequently visits a house.

[0168] The history information of a person detected using sensor information is information indicating the history of detection of the person using sensor information, as described above. As described above, this sensor information may be sensor information generated by, for example, a camera installed outside a house, and may include an image.

[0169] The non-suspicious individual identification unit 360 identifies a non-suspicious individual by using, for example, history information of a person detected using an image.

[0170] In more detail, for example, the non-suspicious person identification unit 360 may identify a person who is detected more frequently than a predetermined first threshold as a non-suspicious person. For example, the non-suspicious person identification unit 360 may identify a person who is detected more frequently than a predetermined first threshold and who performs a predetermined action, such as pressing an intercom, as a non-suspicious person.

[0171] The first threshold may be different for a resident and for a visitor other than a resident. For example, the first threshold for a resident may be larger than the first threshold for a visitor. By using such thresholds, the non-suspicious individual identification unit 360 can distinguish and identify whether a non-suspicious individual is a resident or not.

[0172] That is, the non-suspicious person identification unit 360 may identify a resident by using history information of a person detected using an image. The non-suspicious person identification unit 360 may also identify a visitor who is a non-suspicious person other than a resident by using history information of a person detected using an image.

[0173] Furthermore, for example, the non-suspicious person identification unit 360 may identify as non-suspicious persons persons wearing the same type of clothing who are detected more frequently than a predetermined second threshold value. The non-suspicious person identification unit 360 may also identify as non-suspicious persons persons arriving in the same type of vehicle who are detected more frequently than a predetermined second threshold value.

[0174] In a typical delivery service, delivery personnel may wear the same type of clothing (for example, uniforms). Furthermore, in a typical delivery service, delivery personnel may use the same type of vehicle to make deliveries. Therefore, a person wearing the same type of clothing or a person arriving in the same type of vehicle who is detected more frequently than the second threshold can be identified as a non-suspicious person, such as a delivery personnel, who is unlikely to be suspicious.

[0175] It should be noted that the sensor information for identifying non-suspicious individuals is not limited to sensor information including images. For example, if a resident has a designated path through which to pass when going out or returning home on the premises, and the sensor 10 is an object presence detection sensor that detects the presence or absence of an object in the path, the sensor information generated by the object presence detection sensor may be used to identify non-suspicious individuals.

[0176] The non-suspicious individual identifying unit 360 may also identify a non-suspicious individual using history information of a person detected using multiple types of sensor information.

[0177] (Regarding the presence-at-home information generation unit 350) The presence-at-home information generation unit 350 uses the results of identifying non-suspicious persons as residents from among the non-suspicious persons identified by the non-suspicious person identification unit 360. The presence-at-home information generation unit 350 generates presence-at-home information for residents identified as non-suspicious by the non-suspicious person identification unit 360, using history information of the resident detected using images.

[0178] That is, the sensor information may include an image, and the non-suspicious person may include the resident. The presence-at-home information may be generated using historical information of the resident identified as a non-suspicious person detected using the image.

[0179] The presence-at-home information generating unit 350 may generate presence-at-home information in the same manner as the presence-at-home information generating unit 250 according to the second embodiment.

[0180] (Regarding the suspicious person determination unit 330) The suspicious person determination unit 330 uses the presence information to determine whether or not a person is a suspicious person, for example, from among the detected people, excluding non-suspicious people identified by the non-suspicious person identification unit 360.

[0181] If a passage through an area of ​​the premises where residents do not normally pass is detected, the suspicious person determination unit 330 may determine that a non-resident, non-suspicious person is a suspicious person. The sensor 10 that detects this passage may be any of a camera, an object presence detection sensor, a LiDAR sensor, etc.

[0182] (Operations and Effects) As described above, according to this embodiment, the information processing device 300 further includes the non-suspicious person identification unit 360 that identifies non-suspicious people using history information of people detected using sensor information.

[0183] This makes it possible to appropriately detect suspicious individuals using non-suspicious individuals automatically identified using sensor information, thereby improving safety in homes.

[0184] According to this embodiment, the sensor information includes an image. The non-suspicious individual includes a resident. The presence-at-home information is generated using historical information of a resident who has been identified as a non-suspicious individual and detected using the image.

[0185] This allows automatic generation of presence information that accurately indicates whether residents are at home, and this presence information can be used to appropriately detect suspicious individuals, thereby improving safety in homes.

[0186] According to this embodiment, the suspicious person determination unit 330 determines whether or not a person, excluding non-suspicious persons, from among the detected persons is a suspicious person, using the presence-at-home information.

[0187] This makes it possible to properly detect suspicious individuals while excluding non-suspicious individuals, thereby improving safety in homes.

[0188] Although the present disclosure has been described above with reference to the embodiments, the present 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 the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0189] In addition, although the flowcharts used in the above description show a sequence of steps (processes), the order of steps executed in each embodiment is not limited to the sequence shown in the flowcharts. In each embodiment, the order of steps shown in the diagrams can be changed as long as it does not cause any problems in terms of the content.

[0190] Some or all of the above embodiments may be described as, but are not limited to, the following supplementary notes. 1. An information processing device comprising: suspicious person determination means for determining whether a person detected using sensor information obtained from a sensor monitoring the premises of a house is a suspicious person, using presence-at-home information indicating whether a resident of the house is at home; and notification control means for, when the detected person is determined to be the suspicious person, causing a notification unit to issue a notification to at least one of the person determined to be the suspicious person and the resident. 2. The information processing device described in 1., further comprising presence-at-home information generation means for generating the presence-at-home information using at least one of location information of the resident and history information of the person detected using the sensor information. 3. The information processing device described in 1. or 2., further comprising non-suspicious person identification means for identifying a non-suspicious person using history information of the person detected using the sensor information. 4. The sensors are multiple, and the multiple sensors include one or more home sensors installed in association with the house and one or more vehicle sensors installed in a car parked on the premises. The information processing device described in any one of items 1. to 3. 5. The information processing device described in any one of items 1. to 4., wherein the suspicious person determination means further uses at least one of the movement of the detected person, the time period when the person was detected, the attributes of the detected person, and the circumstances surrounding the detected person to determine whether the detected person is a suspicious person. 6. The information processing device described in item 2., wherein the sensor information includes an image, and the presence-at-home information is generated using history information of the resident detected using the image. 7. The information processing device described in item 3., wherein the suspicious person determination means determines whether a person, excluding non-suspicious persons, from the detected people is a suspicious person using the presence-at-home information.8. An information processing system comprising: a sensor that monitors the premises of a house; analysis means that detects a person present on the premises using sensor information generated by the sensor; suspicious person determination means that determines whether the detected person is a suspicious person using presence information that indicates the presence status of a resident of the house; and notification control means that, when the detected person is determined to be the suspicious person, causes a notification unit to issue a notification to at least one of the person determined to be a suspicious person and the resident. 9. The information processing system described in 8., further comprising presence information generation means that generates the presence information using at least one of location information of the resident and history information of the person detected using the sensor information. 10. The information processing system described in 8. or 9., further comprising non-suspicious person identification means that identifies a non-suspicious person using history information of the person detected using the sensor information. 11. There are multiple sensors, and the multiple sensors include one or more house sensors installed in association with the house and one or more vehicle sensors installed in a car parked on the premises. The information processing system described in any one of items 8 to 10. 12. The information processing system described in any one of items 8 to 11., wherein the suspicious person determination means further uses at least one of the movement of the detected person, the time period when the person was detected, the attributes of the detected person, and the circumstances surrounding the detected person to determine whether the detected person is a suspicious person. 13. The information processing system described in item 9., wherein the sensor information includes an image, and the presence-at-home information is generated using the history information of the resident detected using the image. 14. The information processing system described in item 10., wherein the suspicious person determination means determines whether a person, excluding non-suspicious persons, from the detected people is a suspicious person using the presence-at-home information.15. An information processing method in which one or more computers determine whether a person detected using sensor information obtained from sensors monitoring the grounds of a house is a suspicious person using presence information indicating whether a resident of the house is at home, and, if the detected person is determined to be the suspicious person, cause a notification unit to issue a notification to at least one of the person determined to be the suspicious person and the resident. 16. The information processing method described in 15. further generates the presence information using at least one of location information of the resident and history information of the person detected using the sensor information. 17. The information processing method described in 15. or 16. further identifies a non-suspicious person using history information of the person detected using the sensor information. 18. The information processing method described in any one of 15. to 17., in which there are multiple sensors, and the multiple sensors include one or more home sensors installed in association with the house and one or more vehicle sensors installed in a car parked on the grounds. 19. The information processing method according to any one of 15. to 18., wherein determining whether the person is a suspicious person further uses at least one of the movement of the detected person, the time period when the person was detected, the attributes of the detected person, and the circumstances surrounding the detected person to determine whether the detected person is a suspicious person. 20. The information processing method according to 16., wherein the sensor information includes an image, and the presence-at-home information is generated using history information of the resident detected using the image. 21. The information processing method according to 17., wherein determining whether the person is a suspicious person uses the presence-at-home information to determine whether a person is a suspicious person, for those detected among the people excluding non-suspicious people. 22. A program for causing one or more computers to execute the following: determine whether a person detected using sensor information obtained from a sensor monitoring the grounds of a house is a suspicious person using presence information indicating the presence status of the resident of the house; and, if the detected person is determined to be a suspicious person, cause an alarm unit to issue an alarm to at least one of the person determined to be a suspicious person and the resident.23. The program according to 22., further generating the presence-at-home information using at least one of location information of the resident and history information of the person detected using the sensor information. 24. The program according to 22. or 23., further identifying a non-suspicious person using history information of the person detected using the sensor information. 25. The program according to any one of 22. to 24., wherein the sensors are multiple, and the multiple sensors include one or more residential sensors installed in association with the residence and one or more vehicle sensors mounted on vehicles parked on the premises. 26. The program according to any one of 22. to 25., wherein determining whether the person is a suspicious person further uses at least one of the movement of the detected person, the time period when the person was detected, the attributes of the detected person, and the circumstances surrounding the detected person to determine whether the detected person is a suspicious person. 27. The sensor information includes an image, and the presence-at-home information is generated using the history information of the resident detected using the image. 23. 28. The program according to 24., wherein determining whether the person is a suspicious person includes determining whether or not the person is a suspicious person using the presence-at-home information for a person remaining after excluding the non-suspicious person from the detected people.

[0191] This application claims priority based on Japanese Patent Application No. 2024-003144, filed January 12, 2024, the disclosure of which is incorporated herein by reference in its entirety.

[0192] S1 to S3 Information processing system 10 Sensor 50 Analysis device 51 Sensor information acquisition unit 52 Analysis unit 100, 200, 300 Information processing device 130, 330 Suspicious person determination unit 140 Notification control unit 250, 350 Presence information generation unit 360 Non-suspicious person identification unit

Claims

1. A suspicious person determination means for determining whether a person detected using sensor information obtained from a sensor that monitors the site of a house is a suspicious person or not, using occupancy information indicating the occupancy status of the occupants in the house; and a notification control means for causing a notification unit to perform a notification to at least one of the person determined to be the suspicious person and the occupants when the detected person is determined to be the suspicious person. An information processing apparatus comprising the above.

2. The information processing apparatus according to claim 1, further comprising an occupancy information generation means for generating the occupancy information using at least one of the position information of the occupants and the history information of the person detected using the sensor information.

3. The information processing apparatus according to claim 1 or 2, further comprising a non-suspicious person identification means for identifying non-suspicious persons using the history information of the person detected using the sensor information.

4. The sensor is plural, and the plural sensors include one or more house sensors installed in association with the house and one or more in-vehicle sensors mounted on an automobile parked on the site. The information processing apparatus according to claim 1 or 2.

5. The suspicious person determination means further uses at least one of the actions of the detected person, the time zone in which the person was detected, the attributes of the detected person, and the situation around the detected person to determine whether the detected person is a suspicious person. The information processing apparatus according to claim 1 or 2.

6. The sensor information includes an image, and the occupancy information is generated using the history information of the occupants detected using the image. The information processing apparatus according to claim 2.

7. The suspicious person determination means determines whether a person excluding the non-suspicious persons from the detected persons is a suspicious person or not, using the occupancy information. The information processing apparatus according to claim 3.

8. A sensor that monitors the site of a house; an analysis means for detecting a person present on the site using the sensor information generated by the sensor; a suspicious person determination means for determining whether the detected person is a suspicious person or not, using occupancy information indicating the occupancy status of the occupants in the house; and a notification control means for causing a notification unit to perform a notification to at least one of the person determined to be the suspicious person and the occupants when the detected person is determined to be the suspicious person. An information processing system comprising the above.

9. An information processing method in which one or more computers use occupancy information indicating the occupancy status of residents in the house to determine whether a person detected using sensor information obtained from sensors monitoring the house site is a suspicious person, and when it is determined that the detected person is the suspicious person, cause a notification unit to notify at least one of the person determined to be the suspicious person and the resident.

10. A recording medium having recorded thereon a program for causing one or more computers to execute: determining whether a person detected using sensor information obtained from sensors monitoring the house site is a suspicious person, using occupancy information indicating the occupancy status of residents in the house; and when it is determined that the detected person is the suspicious person, causing a notification unit to notify at least one of the person determined to be the suspicious person and the resident.

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