Abstract: The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and/or an externally-generated event.

Abstract: Items with first indicia of corresponding unique identifiers are worn or carried by duty personnel. One of those items worn or carried by personnel involved in an exceptional, significant, or noteworthy incident is retrieved and authenticated, and information or content pertaining to the incident is stored in a database associated with the corresponding unique identifier of the item. Upon being scanned or machine-read the first indicia can activate a software link to the information or content pertaining to the incident. The first indicia, subsequently added second indicia, or additional identification data can be employed for subsequent authentication of the item.

Abstract: The invention discloses an image detection method, apparatus, device and a medium. The method includes: determining one or more edge points in an input image and gradient directions thereof; generating an initial matrix with the same size as the input image, and assigning the same initial value to all matrix elements in the initial matrix; for each pixel point located in an accumulation region of each edge point, assigning a corresponding accumulation value to a matrix element in the initial matrix corresponding to the pixel point, to obtain an accumulation matrix; determining one or more circle-center positions in the input image based on the accumulation matrix; wherein, the accumulation region of each edge point includes a first direction line for accumulation along the gradient direction of the edge point and a second direction line for accumulation along the direction opposite to the gradient direction of the edge point.

Abstract: Methods and systems for detecting keypoints in image data may include an image sensor interface receiving pixel data from an image sensor. A front-end pixel data processing circuit may receive pixel data and convert the pixel data to a different color space format. A back-end pixel data processing circuit may perform one or more operations on the pixel data. An output circuit may receive pixel data and output the pixel data to a system memory. A keypoint detection circuit may receive pixel data from the image sensor interface in the image sensor pixel data format or receive pixel data after processing by the front-end or the back-end pixel data processing circuits. The keypoint detection circuit may perform a keypoint detection operation on the pixel data to detect one or more keypoints in the image frame and output to the system memory a description of the one or more keypoints.

Abstract: Methods and devices for combining a mask with a selectively progressing video stream may include receiving a selection of at least one mask with a mask zone that obscures at least a portion of the video stream. The methods and devices may include receiving a selection to emplace the at least one mask at a first location within the video stream. The methods and devices may include receiving a selection to enable a tracking icon to move the at least one mask to a second location within the video stream while the video stream progresses. The methods and devices may include generating a combined output of the video stream and the selective emplacement and movement of the at least one mask during the video stream progression.

Abstract: An image sensor is positioned such that a field-of-view of the image sensor encompasses at least a portion of a rack storing items. The image sensor generates images of the items stored on the rack. Over a period of time, a tracking subsystem tracks a pixel position of the wrist of a person interacting with items stored on the rack, receives image frames of the angled-view images. The tracking subsystem determines whether an item was interacted with by a person and, if so, the identified item is assigned to the person.

Abstract: Methods and apparatus for inspecting an underwater vehicle. In embodiments, a system receives a SAR image for at least a portion of an exterior surface of an underwater vehicle and performs CCD processing to compare a baseline SAS image for the underwater vehicle with the received SAR image of the underwater vehicle to generate a CCD output corresponding to a measure of similarity of the baseline SAS image and the received SAS image. The system determines whether there was tampering of the underwater vehicle based on the measure of similarity.

Abstract: In one embodiment, an exemplary computer-implemented method of storing point cloud data in an autonomous driving vehicle can include the operations of receiving raw point cloud data from a LiDAR sensor mounted on the autonomous driving vehicle, the raw point cloud data representing cloud data points acquired in response to laser beams emitted at a given angle; retrieving configuration information of the LiDAR sensor, the configuration information including at least a number of laser lines of the LiDAR sensor. The method further includes the operations of constructing, based on the configuration information, a data structure that includes a data entry for each of the cloud data points, the data entry including multiple fields for storing attributes of the cloud data point, each field having a bit width determined based on the configuration information using a predetermined algorithm; and writing the cloud data points to a storage medium using the data structure.

Abstract: A system for accurately positioning augmented reality (AR) content within a coordinate system such as the World Geodetic System (WGS) may include AR content tethered to trackable physical features. As the system is used by mobile computing devices, each mobile device may calculate and compare relative positioning data between the trackable features. The system may connect and group the trackable features hierarchically, as measurements are obtained. As additional measurements are made of the trackable features in a group, the relative position data may be improved, e.g., using statistical methods.

Abstract: Systems and methods for localization for mobile devices are described. Some implementations may include accessing motion data captured using one or more motion sensors; determining, based on the motion data, a coarse localization, wherein the coarse localization includes a first estimate of position; obtaining one or more feature point maps, wherein the feature point maps are associated with a position of the coarse localization; accessing images captured using one or more image sensors; determining, based on the images, a fine localization pose by localizing into a feature point map of the one or more feature point maps, wherein the fine localization pose includes a second estimate of position and an estimate of orientation; generating, based on the fine localization pose, a virtual object image including a view of a virtual object; and displaying the virtual object image.

Abstract: A method of using a conveyor system comprising capturing, with a first camera of the conveyor system, a first image of a server tray and a plurality of hard disks received along a first conveyor belt of the conveyor system, checking, with one or more processors of the conveyor system, a first serial number associated with the server tray and a second set of serial numbers associated with the plurality of hard disks against a reference list of serial numbers, capturing, with a second camera of the conveyor system, a second image of the server tray and the plurality of hard disks, and verifying an integrity status of the server tray and plurality of hard disks based on the second image.

Abstract: In a method of generating a virtual 3D model of a dental site, scan data comprising a plurality of images of a dental site is received during an intraoral scan. An analysis of an image is performed. A representation of a reference object with known properties is identified in the image based on the analysis. A virtual 3D model of the dental site is generated based on the plurality of images. The image and/or the virtual 3D model of the dental site is modified by adding additional data about the reference object to the intraoral image and/or the virtual 3D model based on the one or more known properties of the reference object. If the image is modified, it may be modified prior to generation of the virtual 3D model, and the virtual 3D model may be generated using the modified image.

Abstract: Systems and techniques for the optimized matched filter tracking of space objects are provided. In one aspect, a method of detecting faint objects includes receiving a plurality of telescope images from a telescope imaging system and performing a plurality of computational calculations on the telescope images using a plurality of search parameters to identify one or more objects moving through the telescope images. The number of the computational calculations is reduced by a priori relatively restricting a parameter space of the computational calculations.

Abstract: An apparatus comprises a detection unit detecting an object from an image acquired by a capturing unit; and a determination unit that determines whether or not the object is the same as a main object, wherein the determination unit includes a first determination unit that determines whether or not coordinates of the object candidate and coordinates of the main object satisfy a distance condition; a second determination unit that determines whether or not a difference between the object candidate and the main object is within a predetermined range; and a third determination unit that determines whether or not the first and second determination units have determined in the affirmative a predetermined number of times in a row, and the determination unit determines whether transfer of the main object has occurred based on determination results of the first to third determination units.

Abstract: Systems and techniques are provided for anomalous path detection within cameras' fields of view. Video of the field of view of a camera in an environment may be received from the camera. A person may be detected in the video. Motion of the person in the video may be tracked to generate a motion path. Contextual data for the motion path may be received. The motion path and contextual data may be stored in a camera training data set. A camera model for the camera and the field of view may be generated by inputting the camera training data set to a machine learning system. The camera model for the camera and the field of view may be stored.

Abstract: Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.

Abstract: A gaze point estimation processing apparatus in an embodiment includes a storage configured to store a neural network as a gaze point estimation model and one or more processors. The storage stores a gaze point estimation model generated through learning based on an image for learning and information relating to a first gaze point for the image for learning. The one or more processors estimate information relating to a second gaze point with respect to an image for estimation from the image for estimation using the gaze point estimation model.

Abstract: A method may include receiving, by a virtual assistant of a user device, an input from a user, the virtual assistant being based on software. The method may include obtaining, by the virtual assistant of the user device and via a sensor of the user device, audio information or video information of the user. The method may include determining, by the virtual assistant of the user device, an identity of the user based on the audio information or the video information of the user and a set of facial embeddings and speech embeddings that is correlated with the user, the set of facial embeddings and speech embeddings being generated using a facial embedding model, a speech embedding model, and a sound source localization model. The method may include performing, by the virtual assistant of the user device, an action based on the input and the identity of the user.

Abstract: Systems, methods, and computer-readable media for feedback on and improving the accuracy of super-resolution imaging. In some embodiments, a low resolution image of a specimen can be obtained using a low resolution objective of a microscopy inspection system. A super-resolution image of at least a portion of the specimen can be generated from the low resolution image of the specimen using a super-resolution image simulation. Subsequently, an accuracy assessment of the super-resolution image can be identified based on one or more degrees of equivalence between the super-resolution image and one or more actually scanned high resolution images of at least a portion of one or more related specimens identified using a simulated image classifier. Based on the accuracy assessment of the super-resolution image, it can be determined whether to further process the super-resolution image. The super-resolution image can be further processed if it is determined to further process the super-resolution image.

Abstract: Systems and techniques are provided for modeling three-dimensional (3D) meshes using images. An example method can include receiving, via a neural network system, an image of a target and metadata associated with the image and/or a device that captured the image; determining, based on the image and metadata, first 3D mesh parameters of a first 3D mesh of the target, the first 3D mesh parameters and first 3D mesh corresponding to a first reference frame associated with the image and/or the device; and determining, based on the first 3D mesh parameters, second 3D mesh parameters for a second 3D mesh of the target, the second 3D mesh parameters and second 3D mesh corresponding to a second reference frame, the second reference frame including a 3D coordinate system of a real-world scene where the target is located.

Abstract: A system includes a mobile robot, the robot comprising a sensor; and a server operably connected to the robot over a network, the robot being configured to detect an object by processing sensor data using a convolutional neural network. A pipeline for robotic object detection using a convolutional neural network includes: a system comprising a mobile robot, the robot comprising a sensor, the system further comprising a server operably connected to the robot over a network, the robot being configured to detect an object by processing sensor data using a pipeline, the pipeline comprising a convolutional neural network, the pipeline configured to perform a data collection step, the pipeline further configured to perform a data transformation step, the pipeline further configured to perform a convolutional neural network step, the pipeline further configured to perform a network output transformation step, the pipeline further configured to perform a results output step.

Abstract: A method includes: setting, in a main training network, an auxiliary training network having the same architecture as the main training network, performing data enhancement on an original image to obtain an enhanced image; inputting the original image into the main training network, inputting the enhanced image into the auxiliary training network; determining whether an intersection-over-union value of a second prediction frame generated by the auxiliary training network and a target frame is greater than an intersection-over-union value of a first prediction frame generated by the main training network and the target frame; and in response to the intersection-over-union value of the second prediction frame and the target frame being greater than the intersection-over-union value of the first prediction frame and the target frame, replacing the intersection-over-union value of the first prediction frame and the target frame with the intersection-over-union value of the second prediction frame and the target f

Abstract: A videoconference system is described that generates a video for a room including multiple videoconference participants and outputs the video as part of the videoconference. The videoconference system is configured to generate the video as including a detailed view of one of the multiple videoconference participants located in the room. To do so, the videoconference system detects user devices located in the room capable of capturing video and determines a position of each user device. The videoconference system then detects a user speaking in the room and determines a position of the active speaker. At least one of the user devices is identified as including a camera oriented for capturing the active speaker. Video content captured by one or more user devices is then processed by the videoconference system to generate a detailed view of the active speaker.

Abstract: A traffic safety assistance device that can communicate with a plurality of moving objects and holds moving object information and map information, the moving object information including position information of some of the moving objects. The traffic safety assistance device is configured as follows. When position information of a first moving object and information about a second moving object existing around the first moving object are input from a terminal mounted on the first moving object, the traffic safety assistance device stores, in addition to the input position information of the first moving object, position of the second moving object in association with the position information of the first moving object as the moving object information.

Abstract: Visual-inertial odometry uses visual input from a camera and inertial motion measurements to track the motion of an object. This technique can be applied to surveying urban environments, such as a curb or streetscape, that are impractical to survey with a surveyor's wheel, GPS, or imagery from cars. Making visual-inertial odometry measurements of a curb with a handheld surveying device yields relative measurements from a starting point to an ending point on the curb. These relative measurements can be pinned to an absolute coordinate frame using measurements of gravity made while acquiring the visual-inertial odometry measurements and absolute measurements of the starting and ending points.

Abstract: Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive a plurality of images captured by an image capture device, the plurality of images being representative of an environment of the host vehicle; analyze at least one of the plurality of images to identify a target vehicle in the environment of the host vehicle; receive map information associated with an environment of the host vehicle; determine a trajectory of the target vehicle over a time period based on analysis of the plurality of images; determine, based on the determined trajectory of the target vehicle and the map information, a position of the target vehicle relative to a road in the environment of the host vehicle; and determine a navigational action for the host vehicle based on the determined position of the target vehicle.

Abstract: Systems and methods of tracking objects around an ego vehicle, using state estimation of the objects, where state estimation may include an estimate of a position, velocity, acceleration and bounding box size of the tracked object are disclosed. An object may be detected at one or more poses over time. For each pose, a first bounding box may be generated. The largest bounding box may be determined. For each pose, the first bounding box may be expanded to an expanded bounding box. The expanded bounding box may match the largest bounding box. For each pose, the center of the expanded bounding box may be determined. The object may be tracked according to the center of the expanded bounding box.

Abstract: A gaze tracking system comprising a first camera operable to capture images of a user within an environment, a second camera, having a smaller field of view than the first camera, operable to capture images of at least one of the user's eyes, an eye identification unit operable to identify a location of at least one of the user's eyes from images captured by the first camera, a camera control unit operable to modify the position and/or orientation of the second camera in dependence upon the detected location of the at least one of the user's eyes, so as to cause the second camera to be able to capture images of at least one of the user's eyes, and a gaze direction identification unit operable to identify a gaze direction of the user from images captured by the second camera.

Abstract: Devices and techniques are generally described for selection of an object to follow during a video call. In various examples, a first frame of image data may be received from a camera. First image data representing a first person and second image data representing a second person may be determined. A first priority level may be determined for the first person at a first time. A second priority level for the second person may be determined at the first time. The camera may be controlled to follow the first person based at least in part on the first priority level and the second priority level.

Abstract: Augmented reality (AR) and virtual reality (VR) environment enhancement using an eyewear device. The eyewear device includes an image capture system, a display system, and a position detection system. The image capture system and position detection system identify feature points within a point cloud that represents captured images of an environment. The display system presents image overlays to a user including enhancement graphics positioned at the feature points within the environment.

Abstract: A method for improving the accuracy of a digital medical model of a part of a patient, the method includes obtaining a set of at least 2 medical images of the patient, where an element including a predefined geometry and/or predefined information was attached to the patient during the recording of the medical images; obtaining at least 2 tracking images taken with at least one camera having a known positional relationship relative to the medical imaging device, the tracking images depicting at least part of the element; determining any movement of the element between acquisition of the at least 2 tracking images; and generating the digital medical model from the acquired medical images, wherein the determined movement of the element is used to compensate for any movement of the patient between the acquisition of the medical images.

Abstract: Certain aspects of the present disclosure provide a method for performing multimode inferencing, comprising: receiving machine learning model input data from a requestor; processing the machine learning model input data with a machine learning model using processing hardware at a first power level to generate first output data; selecting a second power level for the processing hardware based on comparing the first output data to a threshold value; processing the machine learning model input data with the machine learning model using the processing hardware at the second power level to generate second output data; and sending second output data to the requestor.

Abstract: A method for using a multi-hop reasoning framework to perform multi-step compositional long-term reasoning is presented. The method includes extracting feature maps and frame-level representations from a video stream by using a convolutional neural network (CNN), performing object representation learning and detection, linking objects through time via tracking to generate object tracks and image feature tracks, feeding the object tracks and the image feature tracks to a multi-hop transformer that hops over frames in the video stream while concurrently attending to one or more of the objects in the video stream until the multi-hop transformer arrives at a correct answer, and employing video representation learning and recognition from the objects and image context to locate a target object within the video stream.

Abstract: A screenless smart toy device may be a block made of natural or wooden material and may include a transparent or semi-transparent panel allowing one or more LEDs, from within the smart toy device, to be visible when illuminated. The smart toy device may read RFID values from various objects, such as tiles having objects depicted thereupon and RFID tags embedded within, and play different sounds and/or illuminate its LEDs with different colors based on the RFID tag that was read. The smart toy device may operate in a variety of modes, such as in a play/explore mode, quiz mode, definition mode, etc. The smart toy device may be configured into different modes and/or language settings by use of a mode card or separate application.

Abstract: An apparatus, method, system and computer readable medium for video tracking. An exemplar crop is selected to be tracked in an initial frame of a video. Bayesian optimization is applied with each subsequent frame of the video by building a surrogate model of an objective function using Gaussian Process Regression (GPR) based on similarity scores of candidate crops collected from a search space in a current frame of the video. A next candidate crop in the search space is determined using an acquisition function. The next candidate crop is compared to the exemplar crop using a Siamese neural network. Comparisons of new candidate crops to the exemplar crop are made using the Siamese neural network until the exemplar crop has been found in the current frame. The new candidate crops are selected based on an updated surrogate model.

Abstract: An unmanned autonomous vehicle assessment and reporting system may implement a boustrophedonic flight pattern for capturing images of a structure to develop a three-dimensional model of the same. A crisscross boustrophedonic flight pattern may include two or more boustrophedonic flight patterns that are at angles relative to one another.

Abstract: The invention discloses a system for controlling the movement of a personal mobility vehicle including a processing unit that receives and processes a location data of one or more obstacles over a period of time, determines a change frequency of change of location of the obstacles during the period of time, and generates a movement state categorization of the obstacles categorizing them into either a dynamic obstacle or a static obstacle. The processing unit further determines a dynamic distance traveled by the dynamic obstacle during the period of time, the velocity of the dynamic obstacle, determines movement probability data that relates to the probability of movement of a static obstacle based on the change frequency of change of location, and, determines velocity prediction data of a dynamic obstacle based on the velocity of dynamic obstacles during various time intervals of the time period.

Abstract: A system automatically identifies objects in an environment based on a walkthrough video and an annotated floorplan of the environment. The annotated floorplan indicates locations and types of objects that are expected to be in the environment. The system receives the walkthrough video and generates a 3D model of the environment. The system applies a machine learning model to the walkthrough video to identify regions within frames where objects are captured. After identifying the regions within frames of the walkthrough video that include objects, the system modifies corresponding regions of the 3D model to include the identified objects. For each of the identified objects, the system determines a likelihood of the identified object being present at a location in the environment based on a comparison of the modified 3D model and the annotated floorplan.

Abstract: Described is a system for onboard, real-time, activity detection and classification. During operation, the system detects one or more objects in a scene using a mobile platform and tracks each of the objects as the objects move in the scene to generate tracks for each object. The tracks are transformed using a fuzzy-logic based mapping to semantics that define group activities of the one or more objects in the scene. Finally, a state machine is used to determine whether the defined group activities are normal or abnormal phases of a predetermined group operation.

Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: Systems and methods are disclosed herein for tracking a vulnerable road user (VRU) regardless of occlusion. In an embodiment, the system captures a series of images including the VRU, and inputs each of the images into a detection model. The system receives a bounding box for each of the series of images of the VRU as output from the detection model. The system inputs each bounding box into a multi-task model, and receives as output from the multi-task model an embedding for each bounding box. The system determines, using the embeddings for each bounding box across the series of images, an indication of which of the embeddings correspond to the VRU.

Abstract: A computer implemented method for pose estimation of an image sensor includes receiving an omnidirectional image of a scene captured by an image sensor; using a trained neural network to generate a rotation equivariant feature map from the omnidirectional image of the scene; and determining information relating to the pose of said camera when capturing the scene from the rotation equivariant feature map. The rotation equivariant feature map is a SO(3) indexed feature map.

Abstract: An autonomous vehicle uses machine learning based models to predict hidden context attributes associated with traffic entities. The system uses the hidden context to predict behavior of people near a vehicle in a way that more closely resembles how human drivers would judge the behavior. The system determines an activation threshold value for a braking system of the autonomous vehicle based on the hidden context. The system modifies a world model based on the hidden context predicted by the machine learning based model. The autonomous vehicle is safely navigated, such that the vehicle stays at least a threshold distance away from traffic entities.

Abstract: An access control and user tracking system includes ranging cameras installed in thresholds of access points for generating three dimensional models of users passing through the access points in order to detect unauthorized individuals or hand gestures of the users, which can indicate unsafe conditions or that lights and/or equipment should be turned on. The ranging cameras can be point range finding measurement sensor scanning cameras, structured light cameras, or time of flight cameras and can be installed along the top or in the corners of the thresholds.

Abstract: It is possible to receive a point cloud as input and perform shape completion with high accuracy. A shape completion unit inputs an input point cloud and a class identification feature output by a class identification unit to a generator that is learned in advance and generates a shape completion point cloud that is to complete a point cloud and is a set of three-dimensional points by receiving, as input, the point cloud and the class identification feature, gaining an integration result obtained by integrating a global feature that is a global feature based on local features extracted from respective points of the point cloud with the class identification feature, and convoluting the integration result, and outputs the shape completion point cloud that is to complete the input point cloud.

Abstract: Provided is an information processing device including an acquisition unit that acquires a first captured image, a second captured image, and a distance to a subject, and a derivation unit that derives an imaging position distance which is a distance between the first imaging position and the second imaging position, on the basis of a plurality of pixel coordinates for specifying a plurality of pixels of more than three pixels which are present in the same planar region as an emission position irradiated with the directional light beam on the real space and correspond to the position on the real space in each of the first captured image and the second captured image which are acquired by the acquisition unit, emission position coordinates which are derived on the basis of the distance acquired by the acquisition unit, a focal length of an imaging lens, and dimensions of imaging pixels.

Abstract: Techniques for occluding displayable content on a portable electronic device. An EDM device of the portable electronic device may capture a world distance map comprising a plurality of distances to a plurality of points. A camera of the portable electronic device may capture a camera image containing the plurality of points. A geospatial position of a GNSS receiver may be detected. A geospatial position of the camera may be calculated based on the geospatial position of the GNSS receiver. An angle sensor may detect an orientation of the camera. A model image may be generated based on a 3D model, the orientation of the camera, and the geospatial position of the camera. The model image and/or the camera image may be occluded based on the world distance map and the 3D model. A superimposed image comprising the camera image and the model image may be generated and displayed.

Abstract: An information processing apparatus includes an image acquisition unit configured to acquire an image, a first detection unit configured to detect a specific object from the image, a second detection unit configured to detect a part of the specific object from the image, an exposure determination unit configured to determine a first exposure amount with respect to the specific object detected by the first detection unit, and to determine a second exposure amount with respect to the part of the specific object detected by the second detection unit, and a parameter determination unit configured to determine a parameter regarding at least one of the first detection unit, the second detection unit, and the exposure determination unit.

Abstract: A computer device, a non-transitory computer storage medium, and a computer-implemented method of pattern recognition utilizing an elastic clustering algorithm. A sequence of input datapoints are assigned to a particular cluster of K clusters based on a distance from a centroid k representing a center of the particular cluster. The centroid k in each of the K clusters is shifted from a first position to a second position closer than the first position from the sequence of input datapoints. A location of the centroid k in each of the K clusters is relaxed from the second position toward an equilibrium point in the particular cluster of the K clusters. The relaxing of the location of the centroid k occurs according to an elasticity pull factor based on a distance between the centroid k of the particular cluster at a time t.

Abstract: A system includes an image processing apparatus configured to perform image reading processing and a management server. The image processing apparatus includes a unit configured to acquire data created by the image reading processing while a user logs in to a registration server communicable with the management server, and a unit configured to transmit the acquired data, wherein the management server includes a unit configured to receive the transmitted data and a unit configured to transmit the received data, and wherein the transmitted data is stored in a storage server communicable with the registration server.