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This paper presents a methodology for accurately representing dawn and dusk lighting conditions (twilight) through photographs and video recordings. Attempting to generate calibrated photographs and video during twilight conditions can be difficult, since the time available to capture the light changes rapidly over time. In contrast, during nighttime conditions, when the sun is no longer contributing light directly or indirectly through the sky dome, matching a specific time of night is not as relevant, as man-made lights are the dominate source of illumination. Thus, the initial setup, calibration and collection of calibrated video, when it is dark, is not under a time constraint, but during twilight conditions the time frame may be narrow. This paper applies existing methods for capturing calibrated footage at night but develops a method for adjusting the footage in the event matching an exact time during twilight is necessary.

Video and photo based photogrammetry software has many applications in the accident reconstruction community including documentation of vehicles and scene evidence. Photogrammetry software has developed in its ease of use, cost, and effectiveness in determining three dimensional data points from two dimensional photographs. Contemporary photogrammetry software packages offer an automated solution capable of generating dense point clouds with millions of 3D data points from multiple images. While alternative modern documentation methods exist, including LiDAR technologies such as 3D scanning, which provide the ability to collect millions of highly accurate points in just a few minutes, the appeal of automated photogrammetry software as a tool for collecting dimensional data is the minimal equipment, equipment costs and ease of use.

Photoscanning photogrammetry is a method for obtaining and preserving three-dimensional site data from photographs. This photogrammetric method is commonly associated with small Unmanned Aircraft Systems (sUAS) and is particularly beneficial for large area site documentation. The resulting data is comprised of millions of three-dimensional data points commonly referred to as a point cloud. The accuracy and reliability of these point clouds is dependent on hardware, hardware settings, field documentation methods, software, software settings, and processing methods. Ground control points (GCPs) are commonly used in aerial photoscanning to achieve reliable results. This research examines multiple GCP types, flight patterns, software, hardware, and a ground based real-time kinematic (RTK) system. Multiple documentation and processing methods are examined and accuracies of each are compared for an understanding of how capturing methods will optimize site documentation.

This paper introduces a method for calculating vehicle speed and uncertainty range in speed from video footage. The method considers uncertainty in two areas; the uncertainty in locating the vehicle’s position and the uncertainty in time interval between them. An abacus style timing light was built to determine the frame time and uncertainty of time between frames of three different cameras. The first camera had a constant frame rate, the second camera had minor frame rate variability and the third had more significant frame rate variability. Video of an instrumented vehicle traveling at different, but known, speeds was recorded by all three cameras. Photogrammetry was conducted to determine a best fit for the vehicle positions. Deviation from that best fit position that still produced an acceptable range was also explored. Video metadata reported by iNPUT-ACE and Mediainfo was incorporated into the study.

Emergency personnel and first responders have the opportunity to document crash scenes while evidence is still recent. The growth of the drone market and the efficiency of documentation with drones has led to an increasing prevalence of aerial photography for incident sites. These photographs are generally of high resolution and contain valuable information including roadway evidence such as tire marks, gouge marks, debris fields, and vehicle rest positions. Being able to accurately map the captured evidence visible in the photographs is a key process in creating a scaled crash-scene diagram. Image rectification serves as a quick and straightforward method for producing a scaled diagram. This study evaluates the precision of the photo rectification process under diverse roadway geometry conditions and varying camera incidence angles.