Verizon Connect Development Limited

Verizon Patent and Licensing Inc.

Marco Boschi

Leonardo Taccari

Francesco Sambo

Samuele Salti

Luca De Luigi

Tommaso Mugnai

Andrea Basile

Giovanni Pini

Francesco De Felice

A device may receive a ticket identifier associated with a ticket, a vehicle identifier associated with a vehicle, and video data tracking the vehicle to a parking spot in a parking area. The device may process the video data, with models, to identify the parking spot and a parking spot location, and may associate the ticket identifier, the vehicle identifier, and the parking spot location. The device may receive a starting location of a user device associated with a user of the vehicle, and the ticket identifier based on the user scanning the ticket. The device may determine the parking spot location based on the ticket identifier, and may process the starting location and the parking spot location, with a path model, to calculate a navigation path from the starting location to the parking spot location. The device may provide the navigation path to the user device.

Systems and methods for providing multi-camera vehicle tracking and navigation to a vehicle location

A device may receive, from a first vehicle, video data for video captured of a location associated with an accident for a second vehicle, and may process the video data, with a first model, to generate a sparse point cloud of the location associated with the accident. The device may process the video data, with a second model, to generate depth maps for frames of the video data, and may utilize the depth maps with the sparse point cloud to generate a dense point cloud. The device may process the video data, with a third model, to generate a dense semantic point cloud, and may process the dense semantic point cloud, with a fourth model, to determine a dense semantic overhead view of the location associated with the accident. The device may perform actions based on the dense semantic overhead view.

Systems and methods for utilizing machine learning models to reconstruct a vehicle accident scene from video

Systems and methods for utilizing machine learning and other models to reconstruct a vehicle accident scene from video

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Ravenna, Italy

Marco Boschi