A Method for Lens Distortion Correction of Algorithmically Altered Images Talas Engineering

Format:

Book

Conference/Event

Author/Creator:

Pittman, Kathleen, author.

Contributor:

Buckman, Taylor

Mockensturm, Eric

White, Kirsten

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Photogrammetry is a commonly used type of analysis in accident reconstruction. It allows the location of physical evidence, as shown in photographs and video, and the position and orientation of vehicles, other road users, and objects to be quantified. Lens distortion is an important consideration when using photogrammetry. Failure to account for lens distortion can result in inaccurate spatial measurements, particularly when elements of interest are located toward the edges and corners of images. Depending on whether the camera properties are known or unknown, various methods for removing lens distortion are commonly used in photogrammetric analysis. However, many of these methods assume that lens distortion is the result of a spherical lens or, more rarely, is solely due to distortion caused by other known lens types and has not been altered algorithmically by the camera.Today, several cameras on the market algorithmically alter images before saving them. These camera systems use proprietary distortion correction algorithms to change photographs and videos before visually presenting them to the user. Some cameras have been found to produce images where the spherical lens distortion has been partially mitigated, resulting in vertical objects such as power poles appearing straight while horizontal features such as rooflines remain curved. A video or photograph produced by these camera systems can display unexpected distortion patterns that cannot be corrected using spherical lens distortion correction techniques.This paper presents methods for correcting digital camera images that display algorithmically altered distortion mitigation. The methodology is demonstrated using two common dashcams, each implementing a proprietary distortion correction algorithm. The results for each camera are given. The methodology may be applied to any camera system to correct its unique lens distortion

Notes:

Vendor supplied data

Publisher Number:

2025-01-8680

Access Restriction:

Restricted for use by site license