A system that analyzes images of a shelf to determine the available shelf space. Camera images of the shelf from multiple viewpoints may be projected onto the shelf surface to remove distortions from camera projections and to align images to a common shelf reference frame. A mask may be calculated from each projected image that identifies regions that match the appearance of the shelf surface. The shelf surface may have a specific pattern to facilitate identification of these regions. A combined mask may be formed as a union of the masks from individual projected image masks. The available shelf space corresponds to the regions in the combined mask. Combining image masks from multiple viewpoints reduces the effect of occlusion of the shelf surface by items on the shelf.
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une forme; Localisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/88 - Reconnaissance d’images ou de vidéos utilisant des moyens optiques, p.ex. filtres de référence, masques holographiques, filtres de domaine de fréquence ou filtres de domaine spatial
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/50 - Extraction de caractéristiques d’images ou de vidéos en utilisant l’addition des valeurs d’intensité d’image; Analyse de projection
G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
2.
SYSTEM THAT DETERMINES SHELF CONTENTS FROM IMAGES PROJECTED TO THE TOP OF ITEMS
A system that determines the items on a shelf by projecting camera images to a surface aligned with the tops of the items. Projecting the images to the top surface removes distortions due to camera projections and aligns multiple images to a common reference frame. Item tops may be visible without occlusion in one or more camera images, simplifying item identification. The projected images may be input into an item detector that is trained to recognize images of the tops of items. The item detector may process projected images from different cameras with parallel feature extractor subnetworks that generate feature maps; the feature maps may then be averaged across images and the averaged feature map may be input into an item detection subnetwork.
G06V 20/52 - Activités de surveillance ou de suivi, p.ex. pour la reconnaissance d’objets suspects
G06V 10/12 - Acquisition d’images - Détails des dispositions d’acquisition; Leurs détails structurels
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
3.
SYSTEM THAT FITS A PARAMETERIZED THREE-DIMENSIONAL SHAPE TO MULTIPLE TWO-DIMENSIONAL IMAGES
System that analyzes images of an item from multiple viewpoints to construct a parameterized three-dimensional shape that models the item's shape. The system may search for parameter values that minimize a cost function that measures differences between the observed item images and those that would be expected with those parameter values. One illustrative cost function may measure differences between binary image masks associated with the images and projections of the parameterized shape onto each associated image reference frame. Another illustrative cost function may measure differences between colors from different images at points that are projected from the parameterized surface. These two cost functions may be used together to successively derive the parameterized shape. A byproduct of the shape estimation may include a texture map for the appearance of the item, which may be used for example to read and analyze data from an item label.
System that analyzes data from a smart shelf that is monitored by weight sensors and cameras to identify items that are removed from the shelf and the locations of these items on the shelf. By using multiple shelf weight sensors, the location of items removed from or added to a shelf can be calculated from static equilibrium conditions. This weight-based location can be compared to regions of visual change in camera images to cross-check the location of events and to improve accuracy. The location of an item change may also be used in conjunction with a planogram to determine the item expected to be at this location; the expected item can be compared to the item identified using image analysis to further increase item identification accuracy. Weight changes can also be used to determine the quantity of items taken from a shelf.
A case with an integrated or attached credential reader; when a user presents a valid credential (such as a biometric identity like a fingerprint, handprint, or face), the case is unlocked and the user can take items from the case. Sensors in the case such as cameras and weight sensors detect items taken by the user; these are automatically charged to the user or added to the user's shopping cart. The credential reader may be on a door handle so that as the user reaches for the handle the credential is automatically captured. The entire shopping experience may be quick and seamless since the user's credential may be captured automatically and the items the user takes may be accounted for automatically. Embodiments may have a door that opens automatically when the credential is accepted and closes and locks automatically when sensors detect that the user has retracted from the case.
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
G07C 9/00 - Enregistrement de l’entrée ou de la sortie d'une entité isolée
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p.ex. lecture de la lumière blanche réfléchie
G06K 7/10 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire
G06K 19/07 - Supports d'enregistrement avec des marques conductrices, des circuits imprimés ou des éléments de circuit à semi-conducteurs, p.ex. cartes d'identité ou cartes de crédit avec des puces à circuit intégré
G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p.ex. forme, nature, code
6.
Multi-lighting conditions rapid onboarding system for visual item classification
System that facilitates rapid onboarding of an autonomous (cashier-less) store by capturing images of the store's items from multiple angles, with varying background colors, and that builds a classifier training dataset from these images. Background surfaces may for example be coated with retroreflective tape or film, and variable-color incident light sources may generate the desired background colors. Embodiments may automatically rotate or otherwise reorient the item placed in the onboarding system, so that a relatively small number of cameras can capture views from multiple angles. When an item is placed in the system, a fully automated process may generate a sequence of item orientations and background colors, and may capture and process images from the cameras to create training images. Images of the item from multiple angles, under varying lighting conditions, may be captured without requiring an operator to move or reorient the item.
H04N 23/56 - Caméras ou modules de caméras comprenant des capteurs d'images électroniques; Leur commande munis de moyens d'éclairage
H04N 23/90 - Agencement de caméras ou de modules de caméras, p. ex. de plusieurs caméras dans des studios de télévision ou des stades de sport
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
H04N 5/272 - Moyens pour insérer une image de premier plan dans une image d'arrière plan, c. à d. incrustation, effet inverse
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
H04N 23/74 - Circuits de compensation de la variation de luminosité dans la scène en influençant la luminosité de la scène à l'aide de moyens d'éclairage
G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
An automated store that calculates a confidence score for virtual shopping carts of shoppers, and selects carts for manual review based on these scores. Carts with low confidence scores may be more likely to contain errors, so prioritizing manual review of these carts is a cost-effective method of improving overall accuracy. A cart confidence score may be a function of factors such as confidence in the trajectory of the shopper generated by the store tracking system, confidence in the events (such as taking an item from a shelf) that affect the cart, and confidence that events are attributed to the correct shopper. Situations that make tracking, item identification, or attribution more complex may reduce confidence levels. For example, attribution confidence may be low when multiple shoppers are near an event, and item confidence may be low if the probabilistic classifier that identifies the item assigns nontrivial probabilities to multiple items.
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
8.
Multi-angle rapid onboarding system for visual item classification
System that facilitates rapid onboarding of an autonomous (cashier-less) store by capturing images of the store's items from multiple angles, with varying backgrounds, and that builds a classifier training dataset from these images. The system may have cameras in different positions, and backgrounds that generate different background colors. It may have a platform for items that can be switched between a transparent and non-transparent state; cameras below the platform may therefore capture images of the bottom side of the item when the platform is transparent. When an item is placed in the imaging system, a fully automated process may generate a sequence of background colors and may capture and process images from all of the cameras to create training images. Images of the item from multiple angles, including views of the entire external surface of the item, may be captured without requiring an operator to move or reorient the item.
System that allows devices of an autonomous store, such as sensors in product display areas, to receive power and communicate data over conductive rails of store fixtures. By using fixtures to transmit data and power, the need for cabling to these devices, or for batteries to power the devices, is eliminated or greatly reduced, thereby dramatically simplifying installation and maintenance. Illustrative fixtures over which devices can communicate include slatwalls, pegboards, and rectangular support bars. Power and data may be multiplexed onto the same pair of conductive rails. Embodiments may use device hubs to coordinate communication with devices and to act as gateways between devices and centralized store servers. Devices may communicate their identities and locations to store servers to facilitate installation; for example, they may display their identities on electronic labels that are imaged by store cameras, so that the store server can learn the location of each device automatically.
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c. à d. systèmes dans lesquels le signal vidéo n'est pas diffusé
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
An autonomous store that tracks shopper movements and actions, and performs store cleaning actions based on analysis of shopper activity. Cleaning actions may include disinfecting the store or regions within the store using radiation, fogging or spraying, or ventilation. Cleaning actions may be targeted; for example, zones where shoppers linger or congregate may be cleaned more frequently or intensively, or shelves or items that shoppers touch may be cleaned after these interactions. Shopper activity information may be used to limit the number of shoppers in a store at once, for example by denying entry when the store is at capacity. The density of shoppers in regions of the store may be communicated to shoppers so that they can limit their interactions with other shoppers. Shopper activity history may be used for contact tracing by identifying other shoppers that may have been exposed to an individual who was in the store.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A sensor bar shelf monitor, for example that may be added to an existing shelving system to convert a store to autonomous operation. The sensor bar may contain distance sensor to detect shoppers reaching towards items on a shelf, and cameras to determine which items shoppers have taken. It may be installed into shelf supports such as gondola shelving uprights. The sensor bar may be located at the front edge of a shelf, and may monitor the shelf below. Placing the sensor bar along the front edge prevents damage to electronics from spills or shelf cleaning, and prevents heat from the sensor bar electronics from damaging items on the shelf. The sensor bar may have a local sensor bar processor that collects sensor data; images may be analyzed locally or transferred to more powerful store processors. A sensor bar may also have controllable lights and controllable electronic labels.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
A system that integrates camera images and quantity sensors to determine items taken from, placed on, or moved on a shelf or other area in an autonomous store. The items and actions performed may then be attributed to a shopper near the area. Shelves may be divided into storage zones, such as bins or lanes, and a quantity sensor may measure the item quantity in each zone. Quantity changes indicate that a shopper has taken or placed items in the zone. Distance sensors, such as LIDAR, may be used for shelves that push items towards the front. Strain gauges may be used for bins or hanging rods. Quantity changes may trigger analysis of camera images of the shelf to identify the items taken or replaced. Images from multiple cameras that view a shelf may be projected to a vertical plane at the front of the shelf to simplify analysis.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
H04N 5/247 - Disposition des caméras de télévision
G01G 19/14 - Appareils ou méthodes de pesée adaptés à des fins particulières non prévues dans les groupes pour peser des charges suspendues
G01B 7/16 - Dispositions pour la mesure caractérisées par l'utilisation de techniques électriques ou magnétiques pour mesurer les déformations dans un solide, p.ex. au moyen d'une jauge de contrainte à résistance
G01S 17/08 - Systèmes déterminant les données relatives à la position d'une cible pour mesurer la distance uniquement
13.
System having a bar of relocatable distance sensors that detect stock changes in a storage area
A system having a bar containing distance sensors, such as LIDARs, that may be installed behind a shelf in an automated store. Shelves may be divided into storage zones, such as bins or lanes, and a distance sensor may measure the item quantity in each zone. Quantity changes indicate that a shopper has taken or placed items in the zone. Quantity changes may trigger analysis of camera images of the shelf to identify the items taken or replaced. The distance sensor elements within the bar may be relocatable so they can be positioned correctly behind the corresponding storage zones. The bar may have mounting mechanisms on either side for attachment to shelving support structures. These mounting mechanisms may include security locks. The bar may rotate relative to the mounting mechanisms to provide access to the shelf from behind.
G06Q 10/08 - Logistique, p.ex. entreposage, chargement ou distribution; Gestion d’inventaires ou de stocks
G01S 7/481 - Caractéristiques de structure, p.ex. agencements d'éléments optiques
G01S 17/10 - Systèmes déterminant les données relatives à la position d'une cible pour mesurer la distance uniquement utilisant la transmission d'ondes à modulation d'impulsion interrompues
G01S 17/88 - Systèmes lidar, spécialement adaptés pour des applications spécifiques
14.
Rapid onboarding system for visual item classification
System that facilitates rapid onboarding of an autonomous (cashier-less) store by capturing images of items in the store's catalog from different angles, with varying backgrounds and lighting conditions, and that automatically builds a classifier training dataset from these images. The system may have cameras in different positions, lights supporting variable illumination, and monitor screens that generate different background colors. It may have an input device such as a barcode reader, and an operator terminal that prompts operators to place items into the imaging system in the necessary orientations. Once an item is placed in the imaging system, a fully automated process may generate a sequence of background colors, a sequence of lighting conditions, and may capture and process images from all of the cameras to create training images. Training images for an item may be generated in seconds, compared to many minutes per item using manual image capture and processing.
A projected image item tracking system that analyzes projected camera images to determine items taken from, placed on, or moved on a shelf or other area in an autonomous store. The items and actions performed on them may then be attributed to a shopper near the area. Projected images may be combined to generate a 3D volume difference between the state of the area before and after shopper interaction. The volume difference may be calculated using plane-sweep stereo, or using convolutional neural networks. Because these methods may be computationally intensive, the system may first localize a change volume where items appear to have been displaced, and then generate a volume difference only within that change volume. This optimization results in significant savings in power consumption and in more rapid identification of items. The 3D volume difference may also indicate the quantity of items displaced, for example from a vertical stack.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
A system that integrates camera images and quantity sensors to determine items taken from, placed on, or moved on a shelf or other area in an autonomous store. The items and actions performed may then be attributed to a shopper near the area. Shelves may be divided into storage zones, such as bins or lanes, and a quantity sensor may measure the item quantity in each zone. Quantity changes indicate that a shopper has taken or placed items in the zone. Distance sensors, such as LIDAR, may be used for shelves that push items towards the front. Strain gauges may be used for bins or hanging rods. Quantity changes may trigger analysis of camera images of the shelf to identify the items taken or replaced. Images from multiple cameras that view a shelf may be projected to a vertical plane at the front of the shelf to simplify analysis.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
H04N 5/247 - Disposition des caméras de télévision
G01G 19/14 - Appareils ou méthodes de pesée adaptés à des fins particulières non prévues dans les groupes pour peser des charges suspendues
G01B 7/16 - Dispositions pour la mesure caractérisées par l'utilisation de techniques électriques ou magnétiques pour mesurer les déformations dans un solide, p.ex. au moyen d'une jauge de contrainte à résistance
G01S 17/08 - Systèmes déterminant les données relatives à la position d'une cible pour mesurer la distance uniquement
A projected image item tracking system that analyzes projected camera images to determine items taken from, placed on, or moved on a shelf or other area in an autonomous store. The items and actions performed on them may then be attributed to a shopper near the area. Projected images may be combined to generate a 3D volume difference between the state of the area before and after shopper interaction. The volume difference may be calculated using plane-sweep stereo, or using convolutional neural networks. Because these methods may be computationally intensive, the system may first localize a change volume where items appear to have been displaced, and then generate a volume difference only within that change volume. This optimization results in significant savings in power consumption and in more rapid identification of items. The 3D volume difference may also indicate the quantity of items displaced, for example from a vertical stack.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A projected image item tracking system that analyzes projected camera images to determine items taken from, placed on, or moved on a shelf or other area in an autonomous store. The items and actions performed on them may then be attributed to a shopper near the area. Projected images may be combined to generate a 3D volume difference between the state of the area before and after shopper interaction. The volume difference may be calculated using plane-sweep stereo, or using convolutional neural networks. Because these methods may be computationally intensive, the system may first localize a change volume where items appear to have been displaced, and then generate a volume difference only within that change volume. This optimization results in significant savings in power consumption and in more rapid identification of items. The 3D volume difference may also indicate the quantity of items displaced, for example from a vertical stack.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
An autonomous store system that analyzes camera images to track people and their interactions with items using a processor that obtains a 3D model of a store that contains items and item storage areas. Receives images from cameras captured over a time period and analyzes the images and the 3D model of the store to detect a person in the store based on the images, calculates a trajectory of the person, identifies an item storage area proximal to the trajectory of the person during an interaction time period, analyzes two or more images to identify an item within the item storage area that is moved during the interaction time period. The images are captured within or proximal in time to the interaction time period, and the images contain views of the item storage area, and attribute motion of the item to the person. Enables calibration and placement algorithms for cameras.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c. à d. étalonnage de caméra
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
A system that analyzes camera images to track a person from a point where the person obtains an authorization to a different point where the authorization is used. The authorization may be extended in time and space from the point where it was initially obtained. Scenarios enabled by embodiments include automatically opening a locked door or gate for an authorized person and automatically charging items taken by a person to that person's account. Supports automated stores that allow users to enter, take products and exit without explicitly paying. An illustrative application is an automated, unmanned gas station that allows a user to pay at the pump and then enter a locked on-site convenience store or a locked case with products the user can take for automatic purchase. Embodiments may also extend authorization to other people, such as occupants of the same vehicle.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06Q 20/18 - Architectures de paiement impliquant des terminaux en libre-service, des distributeurs automatiques, des bornes ou des terminaux multimédia
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
A system that analyzes camera images to track a person in an autonomous store, and to determine when a tracked person takes or moves items in the store. The system may associate a field of influence volume around a person's location; intersection of this volume with an item storage area, such as a shelf, may trigger the system to look for changes in the items on the shelf. Items that are taken from, placed on, or moved on a shelf may be determined by a neural network that processes before and after images of the shelf. Person tracking may be performed by analyzing images from fisheye ceiling cameras projected onto a plane horizontal to the floor. Projected ceiling camera images may be analyzed using a neural network trained to recognize shopper locations. The autonomous store may include modular ceiling and shelving fixtures that contain cameras, lights, processors, and networking.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c. à d. étalonnage de caméra
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
A robot for capturing image frames of subjects in response to a request includes a base, a robot head, a camera, a vertical positioning mechanism, and a control system. The base has a transport mechanism for controllably positioning the robot along a lateral surface. The vertical positioning mechanism couples the robot head to the base and adjusts a vertical distance between the robot head and the support surface. The control system controls image capture of the camera, the transport mechanism, and the vertical positioning mechanism.
G06T 1/00 - Traitement de données d'image, d'application générale
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels
A robotics camera system includes a camera robot that is in wireless communication with a robot cloud server through a cloud network such as the Internet. A user having a mobile device desires to utilize a particular camera robot. The user mobile device may wirelessly communicate with the camera robot either directly or indirectly, through the cloud network. User interaction with the camera robot may be to initiate a photo session and/or to have the camera robot follow the user through a venue. In some embodiments the user initiates the interaction through a social media platform. In some embodiments the user initiates the interaction by utilizing a designated application that runs upon the user's mobile device. In some embodiments the user initiates the interaction with the use of a two-dimensional machine-readable code element. In some embodiments the user initiates an interaction with a plurality of robots that work collaboratively.
G01B 11/14 - Dispositions pour la mesure caractérisées par l'utilisation de techniques optiques pour mesurer la distance ou la marge entre des objets ou des ouvertures espacés
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G01B 11/06 - Dispositions pour la mesure caractérisées par l'utilisation de techniques optiques pour mesurer la longueur, la largeur ou l'épaisseur pour mesurer l'épaisseur
A robot for capturing image frames of subjects in response to a request includes a base, a robot head, a camera, an angular positioning mechanism, a vertical positioning mechanism, and a control system. The base has a transport mechanism for controllably positioning the robot along a lateral surface. The angular positioning mechanism couples the camera to the robot head and controls a tilt of the camera. The vertical positioning mechanism couples the robot head to the base and adjusts a vertical distance between the robot head and the support surface. The control system controls image capture of the camera, the transport mechanism, the angular positioning mechanism, and the vertical positioning mechanism.
brevets
