Abstract: An information processing apparatus generates structural information of a digest video from a video material. In the information processing apparatus, a video acquisition means acquires a video material. A condition acquisition means acquires a generation condition for generating the digest video from the creator. A generation means generates structural information indicating a structure of the digest video that is generated based on the video material based on the generation condition, and outputs the generated structural information.

Abstract: A learning device includes a learning model for a still image. The learning model includes a mask generation means configured to generate a first object mask identifying an area in which an object exists in a still image, for each individual object. A first parameter including at least one parameter used for processing of generating the first object mask is adjusted based on a first loss. The first loss indicates a difference of the first object mask with respect to a second object mask identifying an area in which an object exists in a moving image including the still image, for each individual object.

Abstract: In order to enhance the accuracy of identifying an object included in an image, an information processing apparatus (10) includes: a first specifying section (11) that specifies, as a region in which the object in the image exists, a plurality of regions having respective different reference positions or respective different reference directions; a second specifying section (12) that specifies an identification region for the object on the basis of the plurality of regions specified by the first specifying section (11); and an identifying section (13) that identifies the object on the basis of the identification region specified by the second specifying section (12).

Abstract: The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.

Abstract: A housing device (20) includes a housing portion (22), a light source (200), and an optical sensor (300). The housing portion (22) includes a holding body (100). An article is housed in the housing portion (22). The holding body (100) extends in one direction. The light source (200) is attached to the holding body (100) along the one direction. The light source (200) applies light toward at least a part of a space (S) located on a side of the holding body (100) with respect to the one direction of the holding body (100), a front of the space (S), and a back of the space (S). The optical sensor (300) is attached to the holding body (100). At least a part of a visual field of the optical sensor (300) faces in at least a part of the space (S), the front of the space (S), and the back of the space (S).

Abstract: An object recognition device (10) includes a holding body (100), a light source (200), and an optical sensor (300). The holding body (100) extends in one direction. The light source (200) is attached to the holding body (100) along the one direction. The light source (200) applies light toward at least a part of a space (S) located on a side of the holding body (100) with respect to the one direction of the holding body (100), a front of the space (S), and a back of the space (S). The optical sensor (300) is attached to the holding body (100). At least a part of a visual field of the optical sensor (300) faces in at least a part of the space (S), the front of the space (S), and the back of the space (S).

Abstract: An image acquisition unit (11) acquires a recognition processing image. A recognition unit (12) recognizes a product in the recognition processing image based on an estimation model. A registration unit (13) registers a result of the recognition in recognized product information. An output unit (14) outputs a result of the recognition. A correction reception unit (16) receives an input for correcting a result of the recognition. A correction unit (17) changes a result of the recognition to a result of the recognition after a correction, and also stores correction information in which a result of the recognition after a correction and the recognition processing image are associated with each other. A learning unit (18) performs relearning by using the recognition processing image stored as the correction information and updates the estimation model, when a number of the recognition processing image stored exceeds a predetermined value.

Abstract: A training data generation device performs the processes of: acquiring first correct answer data that is correct answer data with respect to each training image of a training image group, the training image group including plural training images to be given the correct answer data, the training image group being divided into plural image subgroups, the first correct answer data being generated in a different situation for each of the plural image subgroups; generating a discriminator with respect to the each of the plural image subgroups, the discriminator being learned based on the each of the plural image subgroups and the first correct answer data corresponding to the each of the plural image subgroups; acquiring a discrimination result outputted by the discriminator for each of the plural training images by inputting the each of the plural training images to the discriminator which is generated based on at least one image subgroup other than an image subgroup to which the each of the training images belon

Abstract: In an information processing device, an acquisition means acquires a video material. An important scene detection means detects an important scene in the video material. An event segment detection means detects an event segment in the video material by using a detection result of the important scene.

Abstract: In an information processing device, an acquisition means acquires a video material. An image recognition means detects an image of a target object from the video material. An event segment detection means detects each event segment in the video material by using a detection result of the image of the target object. A digest video is created by connecting event segments being detected in a time series.

Abstract: In an information processing device, an acquisition means acquires a plurality of videos including a video material and a digest video. A coincident segment detection means detects each coincident segment where the video material and the digest video match with each other in content. A training data generation means generates training data from the video material based on the coincident segment.

Abstract: This invention provides a processing apparatus (10) including a container identification score computation unit (11) that computes an identification score of a container for a product, based on an image to be processed containing the product, a product identification score computation unit (12) that computes an identification score of the product, based on the image to be processed, and a first identification unit (13) that decides identification information of the product, based on the computed identification score of the container and the computed identification score of the product.

Abstract: An information processing apparatus (2000) analyzes a captured image (12) generated by a camera (10) to determine a motion of a person. The camera (10) is a camera that images a display place where an item is displayed. The information processing apparatus (2000) detects a reference position (24) from the captured image (12). The reference position (24) indicates a position of a hand of the person. The information processing apparatus (2000) decides an analysis target region (30) to be analyzed in the captured image (12) using the reference position (24). The information processing apparatus (2000) analyzes the analysis target region (30) to determine the motion of the person.

Abstract: An information processing system (2000) includes a camera (10), a polarization filter (20), a display device (30), and an information processing apparatus (200). The display device (30) includes a display screen (32), and an object (40) is placed on the display screen (32). The camera (10) has an image area in which the display screen (32) is included. The information processing apparatus (200) displays, on the display screen (32), a first display based on a captured image (12) generated by the camera (10). The polarization filter (20) is located between a lens of the camera (10) and the display screen (32) when the lens of the camera (10) faces the display screen (32).

Abstract: An information processing apparatus (2000) analyzes a captured image (12) generated by a camera (10) to determine a motion of a person. The camera (10) is a camera that images a display place where an item is displayed. The information processing apparatus (2000) detects a reference position (24) from the captured image (12). The reference position (24) indicates a position of a hand of the person. The information processing apparatus (2000) decides an analysis target region (30) to be analyzed in the captured image (12) using the reference position (24). The information processing apparatus (2000) analyzes the analysis target region (30) to determine the motion of the person.

Abstract: Provided is an information processing apparatus causing an image identification model to perform learning in consideration between a degree of similarity between an original image and a converted image.

Abstract: Provided is an object type identifying apparatus that is capable of correctly identifying the types of objects held in a hand of a person. This object type identifying apparatus is provided with: a memory storing instructions; a storage device storing information indicating a type of an object at a position of each object; and one or more processors configured to execute the instructions to: acquire a position of an object; determine whether an object is picked up or an object is placed, based on sensor information; when determined that an object is picked up, identify a type of the picked-up object, based on the position of the object acquired and information stored in the storage device; and when determined that an object is placed, update information stored in the storage device, using an image captured by a camera that captures arrangement of each object from a front side.

Abstract: Provided is an object type identifying apparatus that is capable of correctly identifying the types of objects held in a hand of a person. This object type identifying apparatus is provided with: a memory storing instructions; a storage device storing information indicating a type of an object at a position of each object; and one or more processors configured to execute the instructions to: acquire a position of an object; determine whether an object is picked up or an object is placed, based on sensor information; when determined that an object is picked up, identify a type of the picked-up object, based on the position of the object acquired and information stored in the storage device; and when determined that an object is placed, update information stored in the storage device, using an image captured by a camera that captures arrangement of each object from a front side.

Abstract: Provided is an object type identifying apparatus that is capable of correctly identifying the types of objects held in a hand of a person. This object type identifying apparatus is provided with: a memory storing instructions; a storage device storing information indicating a type of an object at a position of each object; and one or more processors configured to execute the instructions to: acquire a position of an object; determine whether an object is picked up or an object is placed, based on sensor information; when determined that an object is picked up, identify a type of the picked-up object, based on the position of the object acquired and information stored in the storage device; and when determined that an object is placed, update information stored in the storage device, using an image captured by a camera that captures arrangement of each object from a front side.

Abstract: In a learning data generation device, an acquisition means acquires a first image corresponding to an image obtained by photographing one or more objects arranged in a predetermined arrangement state, and first information corresponding to information indicating the predetermined arrangement state in the first image. An image deformation means deforms a second image corresponding to an image of a desired object. An image generation means generates a fourth image having an arrangement state that matches or is similar to the predetermined arrangement state indicated by the first image, by pasting a deformed second image to the first image or a third image corresponding to the image of a position where the desired object is arranged. A learning data generation means generates data including the fourth image and second information corresponding to the information indicating the arrangement state of the object in the fourth image, as learning data.