A positioning method includes obtaining an estimated extrinsic parameter of a camera and an intrinsic parameter of the camera, where the estimated extrinsic parameter of the camera is obtained based on world coordinates of a plurality of estimation points in a shooting region of the camera, image coordinates of the plurality of estimation points, and the intrinsic parameter of the camera; obtaining image coordinates of a target in an image shot by the camera; and obtaining world coordinates of the target based on the estimated extrinsic parameter of the camera, the intrinsic parameter of the camera, and the image coordinates of the target.

A navigation system includes: a user interface configured to: receive a sensor data packet for a scan area; a control circuit, coupled to the user interface, configured to: analyze the sensor data packet submitted to a multilayer neural network already trained including storing the sensor data packet; parse the sensor data packet with the multilayer neural network to generate real world endpoints and a bounding box including identifying the real world coordinates; apply line rules, including identifying a parking space by the real world endpoints including storing the real world endpoints in the storage circuit; compile an overhead depiction including boundary lines of the parking space identified by the real world endpoints and the bounding box in the scan area; merge vehicle maneuvering instructions into the overhead depiction for accessing the parking space; and present the overhead depiction for displaying on a user display.

A method and apparatus for generating an image of vehicle surroundings are disclosed, including: capturing vehicle surroundings by vehicle cameras arranged on a vehicle body of a vehicle, and generating camera images by the cameras. The camera images of adjacent cameras have overlapping image regions), generating a virtual representation of the surroundings in a virtual three-dimensional space, during which the camera images are projected onto a virtual projection surface in the space. A non-stationary virtual camera in the virtual space determines a position and/or orientation thereof. A first selection region is placed on the surface in a first overlapping image region depending on a virtual camera field of vision, at least one image parameter of a first vehicle camera in the first selection region is calculated, and at least one second vehicle image parameter is adjusted to the at least one first vehicle image parameter in the first selection region.

An image processing device includes: a solid object extraction unit that divides each of a plurality of captured images into a foreground image part occupied by an existent object and a background image part; a background complementing unit that complements each background image part; a background image synthesis unit that generates a background bird's eye synthetic image by performing bird's eye transformation and combining together the background image parts after undergoing the bird's eye transformation; a solid object recognition unit that acquires posture information on the existent object; a solid object projection unit that acquires a three-dimensional virtual object by using the posture information; a three-dimensional space superimposition unit that generates a three-dimensional space image by superimposing the three-dimensional, virtual object on the background bird's eye synthetic image; and a display image output unit that outputs a bird's eye synthetic image generated based on the three-dimensional space image.

A vehicle control device includes a parking space candidate detection unit configured to detect a parking space candidate that is a candidate for a parking space where an own vehicle is parked, using a vehicle peripheral image acquired from a camera; a leaving path detection unit configured to detect, using the vehicle peripheral image, a leaving path on which another vehicle that is parked in an other vehicle parking space located in a periphery of the parking space candidate leaves the other vehicle parking space; and a nuisance parking determination unit configured to perform nuisance parking determination including determining, for each detected parking space candidate, whether the parking space candidate overlaps with the detected leaving path, and thereby classifying the parking space candidate into a nuisance parking space that overlaps with the leaving path or a non-nuisance parking space that avoids overlapping with the leaving path.

A point cloud display method includes determining, from a first point cloud, points describing a target object, where the first point cloud describes a surrounding area of a vehicle in which the in-vehicle system is located, and the target object is to be identified by the in-vehicle system, generating a second point cloud based on the points, and displaying the second point cloud.

The invention relates to a parking spot detection method and system. The method includes: acquiring a parking lot image including one or more idle and occupied parking spots around a vehicle; inputting the parking lot image to a neural network model for image processing, to obtain center point coordinates and a center point probability of the parking spot, first coordinates and first corner point probabilities of four corner points, and offsets of the four corner points relative to the center point coordinates; calculating second coordinates of the four corner points based on the center point coordinates and the offsets of the four corner points relative to the center point coordinates; correcting the second coordinates of the four corner points by using the first coordinates of the four corner points, to obtain actual coordinates and actual corner point probabilities of the four corner points; and performing parking spot detection based on the center point coordinates and the center point probability, and the actual coordinates and actual corner point probabilities of the four corner points. According to the invention, the parking spot can be accurately detected.

The present disclosure provides a spatial parking place detection method and device, a storage medium and a program product, which relate to the field of data processing and, in particular, to the fields of computer vision, autonomous parking and autonomous driving. A specific implementation lies in: acquiring ultrasonic data around a vehicle collected by an ultrasonic sensor on the vehicle, and image data around the vehicle collected by an image collection apparatus; determining a first spatial parking place around the vehicle according to the ultrasonic data, and determining a second spatial parking place around the vehicle according to the image data; fusing the first spatial parking place and the second spatial parking place that are located at an identical position to determine a spatial parking place at that position; and checking the availability of the detected spatial parking place, and determining available spatial parking places of the vehicle.

A method for detecting unmanned aerial vehicles (UAV) includes detecting an unknown flying object in a monitored zone of air space. An image of the detected unknown flying object is captured. The captured image is analyzed to classify the detected unknown flying object. A determination is made, based on the analyzed image, whether the detected unknown flying object comprises a UAV. In response to determining that the detected unknown flying object comprises a UAV, one or more radio signals exchanged between the UAV and a user of the UAV are suppressed until the UAV departs from the monitored zone of air space.

A method of controlling a lighting fixture based on motion detection includes: receiving a plurality of captured image frames; obtaining a plurality of resampled image frames by resampling the captured image frames according to regional characteristics of the resampled image frames; dynamically adjusting a sensitivity for motion detection according to the regional characteristics of the resampled image frames; performing motion detection on the resampled image frames according to the sensitivity; and controlling the lighting fixture according to a result of the motion detection.