A queue analysis apparatus (2000) estimates a position and an orientation of each object (20) included in a target image (10). The target image (10) is generated by a camera (50) that captures the object (20). The queue analysis apparatus (2000) generates a queue line (40) that expresses, by a linear shape, a queue included in a queue region (30) being a region representing a queue in the target image (10), based on a position and an orientation being estimated for each object (20) included in the queue region (30).

A queue analysis apparatus (2000) estimates a position and an orientation of each object (20) included in a target image (10). The target image (10) is generated by a camera (50) that captures the object (20). The queue analysis apparatus (2000) generates a queue line (40) that expresses, by a linear shape, a queue included in a queue region (30) being a region representing a queue in the target image (10), based on a position and an orientation being estimated for each object (20) included in the queue region (30).

This application provides an image processing method, a network training method, and a related device, and relates to image processing technologies in the artificial intelligence field. The method includes: inputting a first image including a first vehicle into an image processing network to obtain a first result output by the image processing network, where the first result includes location information of a two-dimensional 2D bounding frame of the first vehicle, coordinates of a wheel of the first vehicle, and a first angle of the first vehicle, and the first angle of the first vehicle indicates an included angle between a side line of the first vehicle and a first axis of the first image; and generating location information of a three-dimensional 3D outer bounding box of the first vehicle based on the first result.

An image registration method includes: acquiring a target image comprising a target object; inputting the target image to a preset network model, and outputting position information and rotation angle information of the target object; obtaining a reference image comprising the target object by querying a preset image database according to the position information and the rotation angle information; and performing image registration on the target image and the reference image to obtain a corresponding position of the target object of the target image in the reference image.

An image registration method includes: acquiring a target image comprising a target object; inputting the target image to a preset network model, and outputting position information and rotation angle information of the target object; obtaining a reference image comprising the target object by querying a preset image database according to the position information and the rotation angle information; and performing image registration on the target image and the reference image to obtain a corresponding position of the target object of the target image in the reference image.

The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a ground surface is within the field of view of the camera during taxiing of the aircraft. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera for docking guidance by identifying, within the captured image data, a region on the ground surface corresponding to an alignment fiducial indicating a parking location for the aircraft, determining, based on the region of the captured image data corresponding to the alignment fiducial indicating the parking location, a relative location of the aircraft with respect to the alignment fiducial, and outputting an indication of the relative location of the aircraft to the alignment fiducial.

Systems and methods for generating a virtual view of a virtual camera based on an input image are described. A system for generating a virtual view of a virtual camera based on an input image can include a capturing device including a physical camera and a depth sensor. The system also includes a controller configured to determine an actual pose of the capturing device; determine a desired pose of the virtual camera for showing the virtual view; define an epipolar geometry between the actual pose of the capturing device and the desired pose of the virtual camera; and generate a virtual image depicting objects within the input image according to the desired pose of the virtual camera for the virtual camera based on an epipolar relation between the actual pose of the capturing device, the input image, and the desired pose of the virtual camera.

Systems and methods for generating a virtual view of a virtual camera based on an input image are described. A system for generating a virtual view of a virtual camera based on an input image can include a capturing device including a physical camera and a depth sensor. The system also includes a controller configured to determine an actual pose of the capturing device; determine a desired pose of the virtual camera for showing the virtual view; define an epipolar geometry between the actual pose of the capturing device and the desired pose of the virtual camera; and generate a virtual image depicting objects within the input image according to the desired pose of the virtual camera for the virtual camera based on an epipolar relation between the actual pose of the capturing device, the input image, and the desired pose of the virtual camera.