An identification device includes an acquirer for acquiring a captured image obtained by imaging a predetermined target, a specifier for specifying a linear region extending in a plurality of mutually different directions, from a region of the captured image acquired by the acquirer, the region including the predetermined target, and an extractor for extracting a feature quantity based on the pixel value, from the linear region specified by the specifier.

An information processing apparatus capable of communicating with an external device includes an analysis unit configured to analyze image data and to acquire a second analysis result using a second inference model that is less accurate than a first inference model of an external device when communication with the external device is not possible, a transmission unit configured to transmit the image data to the external device when communication with the external device is possible, and an acquisition unit configured to acquire, from the external device, a first analysis result obtained by analyzing, using the first inference model, the image data transmitted to the external device by the transmission unit. The first inference model and the second inference model are generated by performing machine learning.

An information processing apparatus includes processors configured to detect a plurality of movements of an object from a moving image, generate a first timing that indicates a first movement included in the plurality of movements is detected in the moving image for each of a plurality of time units of the moving image, acquire second timings that indicate the first movement within each of at least one of patterns including the first movement, the second timings indicating when movements occur for each of a plurality of time units of time period, obtain a plurality first similarity values by calculating a first similarity value between the moving image and each of the patterns based on the first timing and each of the second timings, and specify a candidate pattern from the patterns based on the plurality of first similarity values.

The present disclosure relates to a vehicle vision system and a method for identifying an entity for access to a vehicle. The system includes a vehicle having one or more vision sensors coupled therewith. Each of the one or more vision sensors produces a vision data signal. The system includes one or more processors, a memory communicatively coupled with the vehicle and the one or more vision sensors. The one or more processors are operable to execute a first classification algorithm operable to performs an initial validation of the vision data signal. The one or more processors are operable to execute a second classification algorithm to perform a validation of the vision data signal. The one or more processors implement the first classification algorithm and/or the second classification algorithm based on one or more vehicle operational settings.

Methods, systems, and computer-readable media are provided for providing a visual indicator indicative of a frictionless checkout status of at least a portion of a retail shelf. In one implementation, a method may comprise: receiving an output from one or more retail store sensors; based on the output from the one or more retail store sensors, determining a frictionless checkout eligibility status associated with the at least a portion of the retail shelf, wherein the frictionless checkout eligibility status is indicative of whether the at least a portion of the retail shelf includes one or more items eligible for frictionless checkout; and causing a display of an automatically generated visual indicator indicating the frictionless checkout eligibility status associated with the at least a portion of the retail shelf.

A vehicle controller includes a processor configured to: input characteristics extracted from an object region representing a target vehicle in an image obtained by a camera of a vehicle into a light-state classifier to identify a light state that is the state of a signal light of the target vehicle; determine a first control value, based on the result of identification of the light state and the positional relationship between the vehicle and the target vehicle, in accordance with a predetermined rule; input the result of identification of the light state into a control command classifier, which has been trained to output a second control value for controlling the vehicle to avoid a collision between the vehicle and the target vehicle, thereby determining the second control value; and determine an integrated control value to be used for controlling the vehicle, based on the first and second control values.

Methods, systems, and computer-readable media are provided for tracking frictionless shopping eligibility relative to individual shopping receptacles. In one implementation a method comprises: obtaining image data captured using a plurality of image sensors; analyzing the image data to identify a shopper at one or more locations; detecting, based on the analysis of the image data, a first product interaction event involving a first shopping receptacle associated with the shopper and a second product interaction event involving a second shopping receptacle associated with the shopper; based on the detected first product interaction event, determining whether the first shopping receptacle is eligible for frictionless checkout; based on the detected second product interaction event, determining whether the second shopping receptacle is eligible for frictionless checkout; and causing delivery of an indicator identifying which of the first shopping receptacle or the second shopping receptacle is ineligible for frictionless checkout.

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 meal preparation area. The processing device determines one of a meal preparation item or a meal preparation action associated with the current state of the kitchen based on the image data. The processing device receives order data comprising one or more pending meal orders. The processing device can determine an order preparation error based on the order data and at least one of the meal preparation item or the meal preparation action. The processing device causes the order preparation error to be displayed on a graphical user interface (GUI).

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 meal preparation area. The processing device determines a first quantity of a first ingredient disposed within a first container based on the image data. The processing device determines a meal preparation procedure associated with the first ingredient based on the first quantity. The processing device causes a notification indicative of the meal preparation procedure to be displayed on a graphical user interface (GUI).

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).