A method of processing an image, an electronic device, and a storage medium, which relate to the artificial intelligence field, in particular to fields of computer vision and intelligent transportation technologies. The method includes: determining at least one key frame image in a scene image sequence captured by a target camera; determining a camera pose parameter associated with each key frame image in the at least one key frame image, according to a geographic feature associated with the key frame image; and projecting each scene image in the scene image sequence to obtain a target projection image according to the camera pose parameter associated with the key frame image, so as to generate a scene map based on the target projection image. The geographic feature associated with any key frame image indicates localization information of the target camera at a time instant of capturing the corresponding key frame image.

A method for acquiring a distance from a moving body to an object located in any direction of the moving body includes steps of: an image processing device (a) instructing a rounded cuboid sweep network to project pixels of images, generated by cameras covering all directions of the moving body, onto N virtual rounded cuboids to generate rounded cuboid images and apply 3D concatenation operation thereon to generate an initial 4D cost volume, (b) instructing a cost volume computation network to generate a final 3D cost volume from the initial 4D cost volume, and (c) generating inverse radius indices, corresponding to inverse radii representing inverse values of separation distances of the N virtual rounded cuboids, by referring to the final 3D cost volume and extracting the inverse radii by using the inverse radius indices, to acquire the separation distances and thus, the distance from the moving body to the object.

A simultaneous localization and mapping-based electronic device includes: a data acquisition device configured to acquire external data; a memory; and a processor configured to be operatively connected to the data acquisition device and the memory, wherein the processor is further configured to extract features of surrounding objects from the acquired external data, calculate a score of a registration error of the extracted features when the number of the extracted features is greater than a set number stored in the memory, and select the set number of features from the among the extracted features, based on the calculated score.

To determine spatial orientation of a vehicle, a set of illuminators is mechanically coupled to the vehicle so as to emit light toward a roadway. A set of sensors is mechanically coupled to the vehicle to receive the emitted light as reflected from the roadway. A timer determines times of flight between emission of the light by the set of illuminators and reception of the reflected light by the set of sensors. A processor determines the spatial orientation of the vehicle from a difference in the times of flight.

A system and method are provided for performing operations comprising: receiving one or more images from an image capture device of a medical treatment location; applying a trained machine learning model to the one or more images to detect presence of a patient in the medical treatment location, the trained machine learning model being trained to establish a relationship between one or more features of images of the medical treatment location and patient presence; generating context assessment for the medical treatment location based on the detected presence of the patient; and transmitting, over a network, the context assessment for presentation on a user interface of a client device.

A control apparatus including an extraction unit configured to extract a subject from an image captured by an image capturing apparatus, an estimation unit configured to estimate a skeleton of the subject extracted by the extraction unit and a control unit configured to control an angle of view of the image capturing apparatus based on a result of the estimation by the estimation unit.

An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor, to identify a pixel location within the first frame, and to determine an estimated sensor location for the sensor by applying a homography to the pixel location. The homography includes coefficients that translate between pixel locations in a frame from the sensor and (x,y) coordinates in the global plane. The system is further configured to determine an actual sensor location for the sensor and to determine a location difference between the estimated sensor location and the actual sensor location. The system is further configured to compare the location difference to a difference threshold level and to recompute the homography in response to determining that the location difference exceeds the difference threshold level.

The present disclosure discloses a photography-based 3D modeling system and method, and an automatic 3D modeling apparatus and method, including: (S1) attaching a mobile device and a camera to the same camera stand; (S2) obtaining multiple images used for positioning from the camera or the mobile device during movement of the stand, and obtaining a position and a direction of each photo capture point, to build a tracking map that uses a global coordinate system; (S3) generating 3D models on the mobile device or a remote server based on an image used for 3D modeling at each photo capture point; and (S4) placing the individual 3D models of all photo capture points in the global three-dimensional coordinate system based on the position and the direction obtained in S2, and connecting the individual 3D models of multiple photo capture points to generate an overall 3D model that includes multiple photo capture points.

The present invention relates to a method for deriving a 3D data from image data comprising: receiving, from at least one camera, image data representing an environment; detecting, from the image data, at least one object within the environment; classifying the at least one detected object, wherein the method comprises, for each classified object of the classified at least one objects: determining a 2D skeleton of the classified object by implementing a neural network to identify features of the classified object in the image data corresponding to the classified object; and constructing a 3D skeleton for the classified object, comprising mapping the determined 2D skeleton to 3D.

A calibration support includes a support body (100) configured to mount a calibration element, the calibration element being configured to calibrate a driving assistance system of a vehicle (500); an image acquisition device (200) connected to the support body (100) and configured to acquire an image of the vehicle (500); a processing device (300) provided on the support body (100), electrically connected to the image acquisition device (200), and configured to calculate, according to the image acquired by the image acquisition device (200), the movement position of the support body (100) relative to the vehicle (500) and output a control signal comprising the movement position; and a control device (400) provided on the support body (100), electrically connected to the processing device (300), and configured to receive the control signal and control the support body (100) to move.