The disclosed systems and methods provide a novel framework that provides mechanisms for a hands-free, autonomous electrical connection of an electric charger to an electric vehicle (EV), and subsequent charging. The disclosed framework utilizes an automated connection device (ACD) as an intermediary between the charger and the EV. The ACD is configured for automatically determining a precise location of the charging inlet on the EV and then automatically establishing an electrical connection with the EV so that the EV can receive a charge. The ACD performs the disclosed precise positional and directional navigation to the EV inlet based on deep neural network analysis of captured imagery of the inlet. In some embodiments, the images can be modified so as to highlight and/or assist the ACD's navigation towards to the inlet in order to maximize invariance.

A server may exchange data with a plurality of scene equipment. The server acquires a registration request sent by a scene equipment. The server groups the scene equipment according to group identification information in the registration request, acquiring a plurality of groups. The server manages scene equipment of a group in the plurality of groups uniformly according to to-be-processed data reported by the scene equipment of the group. The to-be-processed data include at least one of operating state data or scene data.

A system for refining an item identification model detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system captures images of the item. The system extracts a set of features from at least one of the images. The system identifies the item based on the set of features. The system receives an indication that the item is not identified correctly. The system receives an identifier of the item. The system identifies the item based on the identifier of the item. The system feeds the identifier of the item and the images to the item identification model. The system retrains the item identification model to learn to associate the item to the images. The system updates the set of features based on the determined association between the item and the images.

A system for updating a training dataset of an item identification model determines that an item is not included in a training dataset. In response to determining that the item is not included in the training dataset, the system obtains an identifier of the item. The system detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system captures images of the item. The system extracts a set of features associated with the item from the images. The system associates the item to the identifier and the set of features. The system adds a new entry to the training dataset, where the new entry represents the item labeled with the identifier and the set of features.

A system for identifying items based on aggregated metadata obtains images of an item. The system extracts a set of features from images of the item. The system identifies a first value of a first feature associated with a first image of the item. The system identifies a second value of the first feature associated with a second image of the item. The system aggregates the first value and the second value. The system associates the item to the aggregated first value and the second value, where the aggregated first value and the second value represent the first feature of the item. The system adds a new entry for each image of the item to a training dataset associated with an item identification model.

A device configured to receive a first encoded vector and receive one or more feature descriptors for a first object. The device is further configured to remove one or more encoded vectors from an encoded vector library that are not associated with the one or more feature descriptors and to identify a second encoded vector in the encoded vector library that most closely matches the first encoded vector based on the numerical values within the first encoded vector. The device is further configured to identify a first item identifier in the encoded vector library that is associated with the second encoded vector and to output the first item identifier.

According to one embodiment, a method, computer system, and computer program product for dynamic seating arrangement is provided. The embodiment may include identifying a seating arrangement of a venue. The embodiment may also include identifying an obstruction created by one or more viewers seated in one or more seats within the venue. The embodiment may further include generating a model that minimizes or eliminates the identified obstruction. The embodiment may also include calculating a movement differential for each seat in the identified seating arrangement to replicate the generated model. The embodiment may further include moving each seat according to the calculated movement differential.

Apparatuses, methods, systems, and program products are disclosed for capturing an image and determining whether a switch is in a filtering position or a non-filtering position. When the switch is in the filtering position, the camera apparatus detects a switch position and determines whether the switch is in the filtering position or the non-filtering position. The camera captures the image, including audio/visual signals, while the switch is detected to be in the filtering position and modifies the image to inhibit biometric data collection from the image in response to the switch being in the filtering position when the image is captured.

A device configured to capture a first image of an item on a platform using a camera and to determine a first number of pixels in the first image that corresponds with the item. The device is further configured to capture a first depth image of an item on the platform using a three-dimensional (3D) sensor and to determine a second number of pixels within the first depth image that corresponds with the item. The device is further configured to determine that the difference between the first number of pixels in the first image and the second number of pixels in the first depth image is less than the difference threshold value, to extract the plurality of pixels corresponding with the item in the first image from the first image to generate a second image, and to output the second image.

Commentary rating determination systems and methods determine a commentary rating for commentary about a subject media content event that has been generated by a community member. An exemplary embodiment receives video information acquired by a 360° video camera, identifies a physical object from the received video information, determines a physical attribute associated with the identified physical object, wherein the determined physical attribute describes a characteristic of the identified physical object, compares the determined physical attribute of the identified physical object with a plurality of predefined physical object attributes stored in a database, and in response to identifying one of the plurality of predefined physical object attributes that matches the determined physical attribute, associates the quality value of the identified one of the plurality of predefined physical object attributes with the identified physical object. Then, the commentary rating is determined for the commentary based on the associated quality value.