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Systems and methods for 3D image distification

專利號
US11176414B1
公開日期
2021-11-16
申請人
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY（US IL Bloomington）
發(fā)明人
Elizabeth Flowers; Puneit Dua; Eric Balota; Shanna L. Phillips
IPC分類
G06K9/62; G06K9/42; G06K9/00
技術(shù)領(lǐng)域
3d,2d,image,images,or,computing,matrix,in,2d3d,model
地域： IL IL Bloomington

摘要

Systems and methods are described for Distification of 3D imagery. A computing device may obtain a three dimensional (3D) image that includes rules defining a 3D point cloud used to generate a two dimensional (2D) image matrix. The 2D image matrix may include 2D matrix point(s) mapped to the 3D image, where each 2D matrix point can be associated with a horizontal coordinate and a vertical coordinate. The computing device can generate an output feature vector that includes, for at least one of the 2D matrix points, the horizontal coordinate and the vertical coordinate of the 2D matrix point, and a depth coordinate of a 3D point in the 3D point cloud of the 3D image. The 3D point can have a nearest horizontal and vertical coordinate pair that corresponds to the horizontal and vertical coordinates of the at least one 2D matrix point.

說明書

The data structures may also be used to determine a risk factor of the driver as described herein. For example, a computing device, such as a computing device described for FIG. 1 or 2, may take the data structure 1000, and from the entries, determine that a risk factor for the driver Aaron is 50% because two of the entries indicate “Normal Driving” and two of the entries indicate risky driving (i.e., “Texting” and “Calling”). In other embodiments, weights may be assigned to each of the behavior types so that different behavior types could disproportionately impact the driver's risk factor. For example, the texting entry 1004 could have a more negative impact on the driver Aaron's risk factor than the calling entry 1008 in a model that considered texting while driving a more risky activity than calling while driving.

In other embodiments, the quantity of driver behavior entries are measured and used to develop a driver's risk factor. For example a driver with a greater number of “Normal Driving” entries over a period of time would have a better risk factor than that of a driver that had the same number of entries over the same (or similar) period of time, but with fewer “Normal Driving” entries and some, for example, “Texting” or “Calling” entries. In other embodiments, a driver's risk factor could improve (or worsen) over time as a computing device (e.g., of FIG. 2) averages or otherwise compares the number of safe behavior entries (e.g., “Normal Driving”) with a number of risky behavior entries (e.g., “Texting” or “Calling”).

權(quán)利要求

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