A geographic area mapping system may enable collecting, from a set of mobile devices, radio frequency data, the radio frequency data comprising information about a set of network connections in the geographic area; collecting lidar data for the geographic area; generating a mapping between the collected radio frequency data and the collected lidar data for the geographic area; and providing a visualization of the mapped radio frequency data and lidar data for the geographic area.

An imagery processing system determines pixel color values for pixels of captured imagery from volumetric data, providing alternative pixel color values. A main imagery capture device, such as a camera, captures main imagery such as still images and/or video sequences, of a live action scene. Alternative devices capture imagery of the live action scene, in some spectra and form, and capture information related to pixel color values for multiple depths of a scene, which can be processed to provide reconstruction.

Disclosed is a method for detecting Ophiocephalus argus cantor under intra-class occulusion based on cross-scale layered feature fusion, including image collecting, image processing and network model, where collected images are labeled, image sizes are adjusted to obtain input images, and the input images are input into an object detection network, integrated by convolution and inserted into cross-scale layered feature fusion modules, characterized by including dividing all features input into the cross-scale layered feature fusion modules into n layers, composed of s feature mapping subsets, and fusing features of each feature mapping subset with that of other feature mapping subsets, and connecting; carrying out convolution operation, outputting training result; adjusting network parameters by a loss function to obtain parameters for a network model; inputting final output candidate boxes into a non-maximum suppression module to screen correct prediction boxes, so that prediction result is obtained.

There is provided with an image processing apparatus. A detection unit detects an object from a captured image. A generation unit generates a map representing a correspondence between objects detected in a plurality of captured images. A determination unit matches the objects detected in the plurality of captured images based on the generated map.

Disclosed is an image processing method, electronic device and storage medium. The method includes obtaining feature information of first region in a current image frame, wherein first region includes a region that is determined by performing motion estimation on the current and previous image frames based on optical flow; obtaining feature information of second region in the current image frame, wherein second region includes a region corresponding to pixel points among first pixel points of the current image frame, where its association with pixel points among second pixel points of the previous image frame satisfies a condition; and based on the feature information of first region and that of second region, fusing the previous and current image frames to obtain a processed current image frame, which is used as a previous image frame for a next image frame.

Included are an object identification unit that identifies an identified object in an image; a mapping unit that generates a superimposed image by superimposing target points corresponding to ranging points and superimposing a rectangle surrounding the identified object to the image; an identical-object determination unit that specifies, in the superimposed image, two target points closest to the left and right line segments of the rectangle inside the rectangle; a depth addition unit that specifies, in a space, the positions of two edge points indicating the left and right edges of the identified object based on two ranging points corresponding to the two specified target points, and calculates two depth positions of two predetermined corresponding points different from the two edge points; and an overhead-view generation unit that generates an overhead view of the identified object from the positions of the two edge points and the two depth positions.

In step S11, a color image CIMG is acquired. In step S12, a distance image DIMG is acquired. In step S13, the color image CIMG is projected onto the distance image DIMG. An alignment of the color image CIMG and the distance image DIMG is performed prior to the projection of the color image CIMG. In step S14, it is determined whether or not a basic condition is satisfied. In S15, it is determined whether or not a special condition is satisfied. If a judgement result of the steps S14 or S15 is positive, then in step S16 a first data point and a second data point on the distance image DIMG are associated. If bot of the judgement results of steps S14 and S15 are negative, data points are not associated in step S17.

The present disclosure generally pertains to an object detection circuitry configured to: obtain first feature data which are based on first sensing data of a first sensor; compare the first feature data to a first predetermined feature model being representative of a predefined object, wherein the first predetermined feature model is specific for the first sensor, thereby generating first object probability data; obtain second feature data which are based on second sensing data of a second sensor; compare the second feature data to a second predetermined feature model being representative of the predefined object, wherein the second predetermined feature model is specific for the second sensor, thereby generating second object probability data; and combine the first and the second object probability data, thereby generating combined probability data for detecting the predefined object.

A facial parsing method and apparatus, a facial parsing network training method and apparatus, an electronic device and a non-transitory computer-readable storage medium, which relate to the field of artificial intelligence. The facial parsing method includes inputting a facial image into a pre-trained facial parsing neural network; extracting a semantic feature of the facial image by using a semantic perception sub-network; extracting a boundary feature of the facial image by using a boundary perception sub-network; and processing the cascaded semantic feature and boundary feature by using a fusion sub-network, to obtain a facial region to which each pixel in the facial image belongs. The method can improve the resolution capability of a neural network for boundary pixels between different facial regions of a facial image, thereby improving the precision of facial parsing.

A monitoring system for a vehicle cabin, a vehicle including such a monitoring system and a method for monitoring a vehicle cabin. The monitoring system includes a first sensor unit, a second sensor unit and a control unit. The first sensor unit is configured to generate image data of the vehicle cabin. The second sensor unit is configured to generate non-image data of the vehicle cabin. The control unit is configured to collect the image data and the non-image data and determine based thereon whether an obstacle is in the vehicle cabin. The control unit is further configured to limit an actuation of a subsystem if the obstacle is disruptive for the actuation of the subsystem.