A recognition processor performs recognition processing to recognize an external environment of a mobile object, based on image data acquired by an imaging unit that takes an image of an external environment of the mobile object. The recognition processor includes a plurality of arithmetic cores. The plurality of arithmetic cores include a recognition processing core that performs the recognition processing, and an abnormality detection core that detects an abnormality of a data processing system including the imaging unit and the recognition processor, based on the abnormality of the output from the recognition processing core.

A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A remote media system is located at the location of the activity. A network media processor and services is communicatively coupled with the remote media system. The remote media system includes one or more AI enabled cameras. The AI enabled camera is configured to record the activity. The network media processor is configured to receive an activation request of the AI enabled camera and the validate the record request.

A railroad track recognition apparatus has an index calculating unit, an initial searching unit, a secondary searching unit, and a railroad track recognizing unit. The index calculating unit calculates an index value, for each pixel or pixel group of a captured image. The initial searching unit selects a line segment, having a larger evaluation value obtained from the index values, from a plurality of line segments in a first search area, and the secondary searching unit selects a line segment, having a larger evaluation value obtained from the index values, from a second area or a third area. The railroad track recognizing unit recognizes a railroad track based on reference points of the line segments selected by the initial and secondary searching units.

An information prompt method includes collecting an image of an ambient environment of a user to generate a real-scenario image, obtaining information about a point of interest from an electronic map, identifying the real-scenario image to determine a target object, determining a label of the target object and a display position of the label in the real-scenario image based on the information about the point of interest and position information of the electronic device, and displaying an interface including the label and the real-scenario image, where the label is displayed in an overlay mode at the display position in the real-scenario image.

A venue system of a client device can submit a location request to a server, which returns multiple venues that are near the client device. The client device can use one or more machine learning schemes (e.g., convolutional neural networks) to determine that the client device is located in one of specific venues of the possible venues. The venue system can further select imagery for presentation based on the venue selection. The presentation may be published as ephemeral message on a network platform.

An audio control device for a vehicle is provided. The audio control device includes control circuitry that is communicatively coupled to one or more audio reproduction devices of the vehicle. From one or more sensors associated with the vehicle, the control circuitry receives one or more signals. corresponding to a detection of a plurality of objects in a physical space surrounding the vehicle. The control circuitry determines a scene of the physical space, based on the plurality of objects. The control circuitry determines distance between a first object of the detected plurality of objects and the vehicle, based on the received one or more signals. The control circuitry adjusts one or more audio parameters of the audio reproduction devices, based on the determined scene and the determined distance. The control circuitry controls the audio reproduction devices to reproduce a first audio output based on the adjusted audio parameters.

A surveying apparatus (100) comprising a controller (CPU), the controller (CPU) being configured to: receive an image stream representing a video sequence; determine a camera pose for a second image in the image stream relative a first image in the image stream; match the first image with the second image, based on the camera pose; and generate a three dimensional model based on the image match.

A terrain modeling method that fuses geometric characteristics and mechanical characteristics, and a terrain modeling system are provided in the present invention. The method includes steps of: obtaining color images and depth images of the detected region, performing a terrain semantic segmentation on the color images, and fusing ground semantic information obtained by the semantic segmentation and depth information contained in the depth images at the same time to generate the point clouds; mapping the point clouds to a raster map in a map coordinate system to generate corresponding rasters, and updating the elevation values to the corresponding rasters; and calculating the input images in terms of terra-mechanical characteristics, and updating calculation results to the corresponding rasters to generate a terrain model. A mechanical characteristic parameter is added to a terrain factor, and terrain characteristics are innovatively performed from two dimensions of geometric characteristic and mechanical characteristics.

There is provided method of analyzing a field of crops using a drone. The method initially includes remotely controlling the drone to fly the drone to adjacent the field of crops. The method further includes remotely controlling the drone to land the drone adjacent the crops. The method further includes remotely controlling the drone to drive the drone on the ground along the crops. The method further includes capturing ground-based images of the crops at an upward view angle using a multispectral camera on-board the drone.

An object-collection system is disclosed. The system including a vehicle connected to a bucket, a camera connected to the vehicle, and an object picking assembly configured to pick up objects off of ground. The system further includes a processor that obtains object information for identified objects, guides the object-collection system over a target geographical area toward the identified objects based on the object information, captures images of the ground relative to the object picker as the object-collection system is guided towards the identified objects, identifies a target object in the images, tracks movement of the target object across the images as the object-collection system is guided towards the identified objects, and employs the tracked movement of the target object to instruct the object picker to pick up the target object.