Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: Based on operation trajectory data an operation analysis device identifies all open points indicating positions at which the crusher is opened during the operation period and all close points indicating positions at which a crusher is closed during an operation period, calculates, as a shortest distance, a distance between each open point of the all open points and a close point nearest to the each open point, and identifies, as a sorting destination open point, an open point at which the shortest distance exceeds a first threshold value, identifies data until the crusher grasping the dismantling part moves to the sorting destination and returns to the dismantling target again from among the operation trajectory data as movement data of the crusher having moved in the dismantling operation, and identifies data in which the movement data has been removed from the operation trajectory data as grasping operation data.

Abstract: The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit (401) transforms a plurality of images acquired by an image acquisition unit (407). A reliability level calculation unit (402) calculates a level of reliability representing how small the misalignment between images is. A score calculation unit (405) calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (404), and the level of reliability calculated by the reliability level calculation unit (402). An object detection unit (406) detects an object in the images on the basis of the total score.

Abstract: A data-series group includes data series which is a series of data obtained by observing the same object at discrete times. Time labels are time information added to respective data included in the data-series group. State labels are added to some of the data included in the data-series group. A loss-function control unit determines a loss function to be used for learning based on the time labels and the state labels. A threshold is used to adjust a branch condition of the loss-function control unit. A regressor is a model, and is used to detect an abnormality or predict a remaining life span. A dictionary stores parameters of the regressor. A regressor training unit trains the regressor based on the loss function determined by the loss-function control unit.

Abstract: A surveillance system (1) includes an area information acquisition unit (101), a request information provision unit (102), and a participation consent reception unit (103). The area information acquisition unit (101) acquires information of a surveillance-desired area. The request information provision unit (102) provides participation request information for surveillance that is conducted by using an image capturing unit of a portable terminal in the surveillance-desired area. The participation consent reception unit (103) receives participation consent as a response to the participation request information, from the portable terminal.

Abstract: An information processing apparatus (10) includes an event detection unit (110), an input reception unit (120), and a processing execution unit (130). The event detection unit (110) detects a specific event from video data. The input reception unit (120) receives, from a user, input for specifying processing to be executed. The processing execution unit (103) executes first processing specified by input received by the input reception unit (120), and executes second processing of generating learning data used for machine learning and storing the generated learning data in a learning data storage unit (40).

Abstract: A surveillance system (1) includes an area information acquisition unit (101), a position information acquisition unit (102), a candidate determination unit (103), and a notification unit (104). The area information acquisition unit (101) acquires information of a surveillance-desired area. The position information acquisition unit (102) acquires pieces of position information of a plurality of portable terminals (20), each portable terminal performing surveillance using an image capturing unit. The candidate determination unit (103) determines a candidate portable terminal (20) to be moved to the surveillance-desired area from among the plurality of portable terminals (20) based on the acquired pieces of position information of the plurality of portable terminals (20). The notification unit (104) outputs a notification to the candidate portable terminal requesting to move to the surveillance-desired area.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: An anomaly detection apparatus 100 includes an image transformation unit 103 that calculates an image transformation parameter, based on an inspection image in which an inspection object appears, a reference image indicating a normal state of the inspection object and a parameter for image transformation parameter calculation, and performs image transformation on the inspection image using the image transformation parameter, an image change detection unit 104 that collates the reference image and the image-transformed inspection image using a change detection parameter, and calculates an anomaly certainty factor indicating whether there is a change in a specific region of the inspection image, a change detection parameter learning unit 106 that learns the change detection parameter, based on a difference between a training image indicating a correct answer value of the change and the anomaly certainty factor, and an image transformation parameter learning unit 108 that learns the parameter for image transform

Abstract: A surveillance system (1) includes an area information acquisition unit (101), a position information acquisition unit (102), a candidate determination unit (103), and a notification unit (104). The area information acquisition unit (101) acquires information of a surveillance-desired area. The position information acquisition unit (102) acquires pieces of position information of a plurality of portable terminals (20), each portable terminal performing surveillance using an image capturing unit. The candidate determination unit (103) determines a candidate portable terminal (20) to be moved to the surveillance-desired area from among the plurality of portable terminals (20) based on the acquired pieces of position information of the plurality of portable terminals (20). The notification unit (104) outputs a notification to the candidate portable terminal requesting to move to the surveillance-desired area.

Abstract: The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

Abstract: The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit (401) transforms a plurality of images acquired by an image acquisition unit (407). A reliability level calculation unit (402) calculates a level of reliability representing how small the misalignment between images is. A score calculation unit (405) calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (404), and the level of reliability calculated by the reliability level calculation unit (402). An object detection unit (406) detects an object in the images on the basis of the total score.

Abstract: Disclosed is a model generation device capable of mitigating the risk of overlooking a phenomenon of interest in machine learning. The model generation device determines whether or not a label of a first data is similar to a label of a second data. The model generation device assigns the label of the second data to the first data when determining that the label of the first data is similar to the label of the second data based on a degree of similarity between observation information representing a state where the first data is observed and observation information representing a state where the second data is observed. The model generation device calculates model representing a relevance between data information containing the first data and the second data and label information containing the assigned label and the label of the second data.

Abstract: An information processing apparatus (2000) includes a determination unit (2020) and a notification unit (2040). The determination unit (2020) determines whether a field of view of a second camera is correct, on the basis of a first captured image (40), a second captured image (50), and relationship information (information indicating the relationship to be satisfied between a field of view of a first camera and the field of view of the second camera). In a case in which the field of view of the second camera is not correct, the notification unit (2040) notifies that the field of view of the second camera is not correct. The first camera is provided in a head-mounted display worn by a person. The second camera is provided in a part other than the head-mounted display.

Abstract: The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

Abstract: A surveillance system (1) includes an area information acquisition unit (101), a request information provision unit (102), and a participation consent reception unit (103). The area information acquisition unit (101) acquires information of a surveillance-desired area. The request information provision unit (102) provides participation request information for surveillance that is conducted by using an image capturing unit of a portable terminal in the surveillance-desired area. The participation consent reception unit (103) receives participation consent as a response to the participation request information, from the portable terminal.

Abstract: A surveillance system (1) includes an area information acquisition unit (101), a position information acquisition unit (102), a candidate determination unit (103), and a notification unit (104). The area information acquisition unit (101) acquires information of a surveillance-desired area. The position information acquisition unit (102) acquires pieces of position information of a plurality of portable terminals (20), each portable terminal performing surveillance using an image capturing unit. The candidate determination unit (103) determines a candidate portable terminal (20) to be moved to the surveillance-desired area from among the plurality of portable terminals (20) based on the acquired pieces of position information of the plurality of portable terminals (20). The notification unit (104) outputs a notification to the candidate portable terminal requesting to move to the surveillance-desired area.

Abstract: An information processing apparatus (2000) includes a determination unit (2020) and a notification unit (2040). The determination unit (2020) determines whether a field of view of a second camera is correct, on the basis of a first captured image (40), a second captured image (50), and relationship information (information indicating the relationship to be satisfied between a field of view of a first camera and the field of view of the second camera). In a case in which the field of view of the second camera is not correct, the notification unit (2040) notifies that the field of view of the second camera is not correct. The first camera is provided in a head-mounted display worn by a person. The second camera is provided in a part other than the head-mounted display.