Abstract: Speed of first work is compared with speed of second work based on a first working period when a worker is caused to perform the first work of setting annotation data to first image data and a second working period when the worker is caused to perform the second work of correcting advance annotation data set based on a recognition result obtained by causing a predetermined recognizer to recognize the first image data, and, in a case where the first work is faster than the second work, the worker is requested to correct second image data in which advance annotation data is not set, while, in a case where the second work is faster than the first work, the worker is requested to correct advance annotation data set based on a recognition result obtained by causing the recognizer to recognize the second image data.

Abstract: An annotation device includes: a similarity information obtainer that obtains similarity information indicating whether or not a plurality of labels to be added as annotation data to images are similar to each other; a determiner that determines a layout of the plurality of labels to be displayed on an operation screen for an annotation operation based on the similarity information; a data obtainer communicator that obtains the annotation data added to the images using the operation screen; and an inspector that inspects the annotation data obtained by the second communicator for an erroneously added label.

Abstract: Speed of first work is compared with speed of second work based on a first working period when a worker is caused to perform the first work of setting annotation data to first image data and a second working period when the worker is caused to perform the second work of correcting advance annotation data set based on a recognition result obtained by causing a predetermined recognizer to recognize the first image data, and, in a case where the first work is faster than the second work, the worker is requested to correct second image data in which advance annotation data is not set, while, in a case where the second work is faster than the first work, the worker is requested to correct advance annotation data set based on a recognition result obtained by causing the recognizer to recognize the second image data.

Abstract: A learning data creation method acquires items of still image data or moving image data as event data and non-event data, presents first data that is still image data or moving image data that is of a predetermined time before an event and included in the event data and still image data or moving image data, presents non-event data as second data, receives a judgment result as to whether the first data and second data are similar, (a) stores the event data as positive data, (b) stores the non-event data as positive data if the first data and second data are similar, and (c) stores the non-event data as negative data if the first data and second data are not similar.

Abstract: An orientation identification method for identifying an orientation of a device installed by being mounted on a moving body includes: obtaining a certain amount of acceleration in three mutually orthogonal directions detected by an acceleration sensor included in the device; and identifying the orientation of the device expressed in a coordinate system from acceleration data indicating the certain amount of acceleration obtained in the obtaining, according to movement characteristics indicated by statistics of acceleration during movement of the moving body expressed in the coordinate system, the coordinate system including a gravitational acceleration direction as an axis.

Abstract: An identification method includes acquiring image data, acquiring a photography condition for the image data, selecting a classifier corresponding to the acquired photography condition from a plurality of classifiers, each of which was created for one of a plurality of different photography conditions, and identifying a target in the acquired image data by using the selected classifier.

Abstract: An annotation device includes: a similarity information obtainer that obtains similarity information indicating whether or not a plurality of labels to be added as annotation data to images are similar to each other; a determiner that determines a layout of the plurality of labels to be displayed on an operation screen for an annotation operation based on the similarity information; a data obtainer communicator that obtains the annotation data added to the images using the operation screen; and an inspector that inspects the annotation data obtained by the second communicator for an erroneously added label.

Abstract: In an information collection method of the present disclosure, a processor of an information collection system acquires event sensor data and identification information from an on-vehicle terminal or a passenger terminal, the event sensor data being output, in response to an event trigger, from an on-vehicle sensor mounted on the on-vehicle terminal or a passenger sensor mounted on the passenger terminal, the identification information identifying an output source of the event sensor data, identifies the passenger terminal associated with the identification information when the identification information indicates the on-vehicle terminal, identifies the on-vehicle terminal associated with the identification information when the identification information indicates the passenger terminal, requests sensor data from the identified passenger terminal or on-vehicle terminal, acquires the sensor data from a request destination, and stores the event sensor data and the sensor data in association with each other in a

Abstract: A parking position identification method includes acquiring input data as an image that is generated by photographing a parking region by a camera which is installed in a target vehicle, and identifying a parking position of the target vehicle in the photographed parking region by inputting the input data to a learning model that indicates a relationship between the parking region which has a width in which parking of at least one vehicle is feasible and a parking position for one vehicle in the parking region.

Abstract: A contribution level determination method includes: obtaining, from a first storage device, records of annotation works for each of one or more units of target data for which the annotation works carried out by each of workers that use crowdsourcing have been completed; and calculating work contribution levels for each of worker IDs representing a different one of the workers, in accordance with a predetermined weighting rule stored in a second storage device, by referring to the records. In the calculating of the contribution levels, for each of the one or more units of target data, the work contribution level is calculated using a heavier weight for the worker ID representing a particular worker who carried out the annotation work initially than for the work contribution levels for the worker IDs representing the workers who carried out annotation works after the particular worker, in the order of the annotation works.

Abstract: In an information collection method of the present disclosure, a processor of an information collection system acquires event sensor data and identification information from an on-vehicle terminal or a passenger terminal, the event sensor data being output, in response to an event trigger, from an on-vehicle sensor mounted on the on-vehicle terminal or a passenger sensor mounted on the passenger terminal, the identification information identifying an output source of the event sensor data, identifies the passenger terminal associated with the identification information when the identification information indicates the on-vehicle terminal, identifies the on-vehicle terminal associated with the identification information when the identification information indicates the passenger terminal, requests sensor data from the identified passenger terminal or on-vehicle terminal, acquires the sensor data from a request destination, and stores the event sensor data and the sensor data in association with each other in a

Abstract: A learning data creation method acquires items of still image data or moving image data as event data and non-event data, presents first data that is still image data or moving image data that is of a predetermined time before an event and included in the event data and still image data or moving image data, presents non-event data as second data, receives a judgment result as to whether the first data and second data are similar, (a) stores the event data as positive data, (b) stores the non-event data as positive data if the first data and second data are similar, and (c) stores the non-event data as negative data if the first data and second data are not similar.

Abstract: An orientation identification method for identifying an orientation of a device installed by being mounted on a moving body includes: obtaining a certain amount of acceleration in three mutually orthogonal directions detected by an acceleration sensor included in the device; and identifying the orientation of the device expressed in a coordinate system from acceleration data indicating the certain amount of acceleration obtained in the obtaining, according to movement characteristics indicated by statistics of acceleration during movement of the moving body expressed in the coordinate system, the coordinate system including a gravitational acceleration direction as an axis.

Abstract: An image processing method includes acquiring consecutive time-series images captured by an onboard camera of a vehicle, having a first annotation indicating two or more first regions, and at least including one or more images in which the two or more first regions are on a path of the vehicle and a distance therebetween is smaller than or equal to a threshold; determining, in reverse chronological order from an image of the last time point, positions of the two or more regions in each consecutive time-series image; identifying, from among the consecutive time-series images, the first image of a first time point in which none of the two or more first regions are located on the path, and setting, as a second region, a region between the two or more first regions in the identified first image; and attaching a second annotation to the first image corresponding to the first time point, the second annotation indicating the second region.

Abstract: A remote-operated working device includes a movement controller that controls movement of the remote-operated working device; a working unit that performs a predetermined work on the work target; a first detector that detects contact between the working unit and the target; and a communicator that transmits a signal for vibrating the remote control to the remote control in a case where contact between the working unit and the work target is detected by the first detector.

Abstract: An image processing method includes acquiring consecutive time-series images captured by an onboard camera and including at least one image having a first annotation indicating a first region; determining, for each of the images, in reverse chronological order from an image of the last time point, whether the first region exists in the image based on whether the first annotation is attached; identifying the first image of a first time point for which the first region is determined not to exist, and setting a second region including a partial region of an object in the identified first image, indicating the moving object that is obstructed by the object before appearing on the path, and having dimensions based on dimensions of the first region in an image of a second time point immediately after the first time point; and attaching a second annotation to the image corresponding to the second time point, the second annotation indicating the second region.

Abstract: Techniques of simplifying a process of performing an annotation process and constructing learning data by using CG data are provided. A learning data generation device generates scene data for generating learning data from scene data of CG data that include various models. In the generation, the models other than a specific object model are deleted from the scene data, the specific object model is made a specific color, and a specific object region of the specific object model is set. Then, the image for scene data and information of the specific object region are associated with each other and stored as the learning data in a second storage unit.

Abstract: An adherent detecting method for, by means of at least one computer, detecting a target adherent adhering to a translucent body that separates an imaging element and a photographing target from each other includes acquiring a photographed image that is generated by photographing via the translucent body with the imaging element. Next, the photographed image is inputted as input data into a recognition model for recognizing the presence or absence of an adherent to the translucent body in an image taken via the translucent body. Then, the presence or absence of the target adherent in the photographed image is detected by acquiring information outputted from the recognition model and indicating the presence or absence of an adherent in the photographed image.

Abstract: A moving body detecting method for, by means of at least one computer, detecting a target moving body that is a moving body which possibly constitutes an obstacle to running of a target vehicle includes acquiring a photographed image that is generated by photographing with a camera situated on board the target vehicle. Next, the photographed image is inputted as input data into a recognition model for recognizing a moving body in an image taken of the moving body, type information indicating a type of the moving body, and position information indicating that one of a plurality of positions including a sidewalk and a roadway in which the moving body is present. Then, the target moving body in the photographed image is detected by acquiring the type information and position information of the moving body in the photographed image as outputted from the recognition model.

Abstract: An MD management device manages action statistics associated with each of a plurality of shelving units. Action statistics for one shelving unit indicate actions that store visitors make near the shelving unit. An information terminal device acquires, from the MD management device merchandising information for each shelving unit. Merchandising information for one shelving unit includes a value of statistics for the shelving unit. The information terminal device displays a store screen including one or more shelving unit icons each representing one of one or more shelving units located in a store. Further, the information terminal device displays a graphic item in the vicinity of each shelving unit icon. The graphic item is displayed in a display state that is in accordance with the value of statistics for the corresponding shelving unit.