Abstract: Embodiments are generally directed to generating and providing normalized representation in a three-dimensional (3D) model.

Abstract: A method receives situation data including images from vehicles; clusters, into an image cluster, the images included in the situation data of vehicle(s) located in a geographic region from among the vehicles; locates related situation object(s) in image(s) of the image cluster; matches images from different vehicles in the image cluster, the matched images having corresponding feature(s) of the related situation object(s); determines three-dimensional (3D) sensor coordinates of the related situation object(s) relative to a sensor position of a target vehicle associated with at least one matched image, using the corresponding feature(s) of the related situation object(s) in the matched images; converts the 3D sensor coordinates of the related situation object(s) to geolocation coordinates of the related situation object(s) using geolocation data of the different vehicles associated with the matched images; and determines a coverage area of a traffic situation based on the geolocation coordinates of the relate

Abstract: A method receives situation data including images from vehicles; clusters, into an image cluster, the images included in the situation data of vehicle(s) located in a geographic region from among the vehicles; locates related situation object(s) in image(s) of the image cluster; matches images from different vehicles in the image cluster, the matched images having corresponding feature(s) of the related situation object(s); determines three-dimensional (3D) sensor coordinates of the related situation object(s) relative to a sensor position of a target vehicle associated with at least one matched image, using the corresponding feature(s) of the related situation object(s) in the matched images; converts the 3D sensor coordinates of the related situation object(s) to geolocation coordinates of the related situation object(s) using geolocation data of the different vehicles associated with the matched images; and determines a coverage area of a traffic situation based on the geolocation coordinates of the relate

Abstract: In an example embodiment, a computer-implemented method receives image data from one or more sensors of a moving platform and detecting one or more objects from the image data. The one or more objects potentially represent extremities of a user associated with the moving platform. The method processes the one or more objects using two or more context processors and context data retrieved from a context database. The processing produces at least two confidence values for each of the one or more objects. The method filters at least one of the one or more objects from consideration based on the confidence value of each of the one or more objects.

Abstract: In an example embodiment, a computer-implemented method receives image data from one or more sensors of a moving platform and detecting one or more objects from the image data. The one or more objects potentially represent extremities of a user associated with the moving platform. The method processes the one or more objects using two or more context processors and context data retrieved from a context database. The processing produces at least two confidence values for each of the one or more objects. The method filters at least one of the one or more objects from consideration based on the confidence value of each of the one or more objects.

Abstract: The disclosure describes novel technology for inferring scenes from images. In one example, the technology includes a system that can determine partition regions from one or more factors that are independent of the image data, for an image depicting a scene; receive image data including pixels forming the image; classify pixels of the image into one or more pixel types based on one or more pixel-level features; determine, for each partition region, a set of pixel characteristic data describing a portion of the image included in the partition region based on the one or more pixel types of pixels in the partition region; and classify a scene of the image based on the set of pixel characteristic data of each of the partition regions.

Abstract: The disclosure describes novel technology for inferring scenes from images. In one example, the technology includes a system that can determine partition regions from one or more factors that are independent of the image data, for an image depicting a scene; receive image data including pixels forming the image; classify pixels of the image into one or more pixel types based on one or more pixel-level features; determine, for each partition region, a set of pixel characteristic data describing a portion of the image included in the partition region based on the one or more pixel types of pixels in the partition region; and classify a scene of the image based on the set of pixel characteristic data of each of the partition regions.