The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving, by a processing device, image data having one or more image frames indicative of a state of a meal preparation area. The method may further include, determining, based on the image data, a first feature characterization of a first meal preparation item associated with the state of the meal preparation area. The method may further include determining that the first feature characterization does not meet object classification criteria for a set of object classifications. The method may further include causing a notification indicating the first meal preparation item and one of an object classification or a classification status corresponding to the first meal preparation item on a graphical user interface (GUI).

An information processing device that is configured to: receive, from each of two or more vehicles, image information captured by an image capture device installed at a vehicle, and vehicle information including position information on the vehicle; in a case in which a dangerously-driven vehicle has been detected by vehicles, establish a priority level for image processing of image information captured by the vehicles that have detected the dangerously-driven vehicle, in accordance with a predetermined condition; and based on the image information, perform image processing to identify a characteristic of the dangerously-driven vehicle in accordance with the established priority level.

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving, by a processing device, image data including one or more image frames indicative of a current state of a drive-thru area. The processing device determines a vehicle disposed within the drive-thru area based on the image data. The processing device receives order data with a pending meal order. The processing device determines a first association between the vehicle and the pending meal order based on the image data. The processing devices determine a meal delivery procedure associated with the based on the association between the vehicle and the pending meal order. The processing device performs may perform the meal delivery procedure. The processing device may provide the meal delivery procedure for display on a graphical user interface (GUI).

A method for receiving a plurality of captures of a vehicle from a plurality of locations. Calculating a plurality of signatures of the vehicle, each respectfully associated with a corresponding capture from the plurality of captures. Associating the plurality of signatures resulting in a single signature and utilizing the single signature for identifying the vehicle.

The present disclosure provides an image processing device capable of outputting both an image for humans and an image for artificial intelligence, comprising: a display; a memory for storing at least one image frame for artificial intelligence and multiple basic image frames to be outputted as an image through the display; and a processor which controls the display to, while outputting the basic image frames as an image, output the image frame for artificial intelligence as an image in compliance with a configuration condition, wherein the outputted image includes at least one image frame for artificial intelligence inserted between the basic image frames. In accordance with to the present disclosure, a single display can provide both an image for humans and an image which can be recognized by artificial intelligence, whereby the display can be utilized as a platform for communication with artificial intelligence as well as humans.

There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

A system includes one or more video capture devices and a processor coupled to each video capture device. Each processor is operable to direct its respective video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, each bounding perimeter virtual object surrounding at least part of its respective object of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for multiple objects of interest, and may optionally generate an alert at least partially in response to the near-miss condition.

At least one processor of an electronic device in a vehicle may be configured to: receive broadcast information which is broadcast from a beacon and includes reference data indicating the relative positional relationship between a designated object positioned in a designated place and the position of the beacon and the data of the designated place; in response to reception of the broadcast information, acquire sensed data indicating the relative positional relationship between the designated object and the vehicle through at least one sensor of the electronic device on the basis of the data of the designated place; in response to acquiring the sensed data, identify the difference between the sensed data and the reference data; identify whether the difference lies outside of a reference range; and determine correction of the at least one sensor to be required, on the basis of identification that the difference lies outside of the reference range.

A multi-purpose detection circuit for object detection and vehicle position determination is described. For example, the circuit is configurable for detecting foreign metallic objects, living objects, and a vehicle or type of vehicle above an inductive wireless power transmitter. The circuit is also configurable for determining the vehicle's position relative to the inductive wireless power transmitter. An example apparatus includes a measurement circuit including a multiplexer, electrically connected to a plurality of inductive and capacitive sense circuits, for measuring one or more electrical characteristics in each of the inductive and capacitive sense circuits according to a predetermined time multiplexing scheme. The apparatus further includes a control and evaluation circuit for evaluating the measured electrical characteristics and determining at least one of a presence of a metallic object, a living object, a vehicle, or a type of vehicle, and a vehicle position based on changes in the measured electrical characteristics.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.