The Effect of Tracker and Control Point Distributions on Vehicle Position and Speed Estimates from Dash Camera Video

<div class="section abstract"><div class="htmlview paragraph">Dash cameras (dashcams) can provide collision reconstructionists with quantifiable vehicle position and speed estimates. These estimates are achieved by tracking 2D video features with camera-tracking software to solve for the time history of camera position, and speed can then be calculated from the position-time history. Not all scenes have the same geometric features in quality or abundance. In this study, we compared the vehicle position and derived-speed estimates from dashcam video for different numbers and spatial distributions of tracked features that mimicked the continuum between barren environments and feature-rich environments. We used video from a dashcam mounted in a vehicle undergoing straight-line emergency braking. The surrounding environment had abundant trackable features on both sides of the road, including road markings, streetlights, signs, trees, and buildings. We first created a reference solution using SynthEyes, a 3D camera- and object-tracking program, and all available features. We then created eight test conditions by eliminating the available 2D trackers and 3D control points in large volumetric regions to mimic less feature-rich environments. To compare these different conditions, we calculated the bias, uncertainty, and mean absolute error in the camera position and speed estimates between the test conditions and the reference condition. For six of the eight test conditions, we found that removing large regions of the available trackers and control points generated camera solutions with relatively small position errors (mean absolute errors of 32 to 119 mm) and small speed errors (<1 km/h) compared to the reference solution. One test condition did not reach a solution and another test condition with a poor distribution of few control points had larger errors. Overall, these findings showed that the camera-tracking methodology used here for calculating a dashcam’s position and speed remained robust even in some relatively feature-poor environments.</div></div>

Authors

Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-03-31
DOI
https://doi.org/10.4271/2025-01-8697
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Effect of Tracker and Control Point Distributions on Vehicle Position and Speed Estimates from Dash Camera Video

MB Ahrens, Gunter P. Siegmund, Cole Young, Thomas R. Flynn
SAE technical papers on CD-ROM/SAE technical paper series
Autonomous Vehicle Technology and Safety
article

The Effect of Tracker and Control Point Distributions on Vehicle Position and Speed Estimates from Dash Camera Video

MB Ahrens, Gunter P. Siegmund, Cole Young, Thomas R. Flynn
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">Dash cameras (dashcams) can provide collision reconstructionists with quantifiable vehicle position and speed estimates. These estimates are achieved by tracking 2D video features with camera-tracking software to solve for the time history of camera position, and speed can then be calculated from the position-time history. Not all scenes have the same geometric features in quality or abundance. In this study, we compared the vehicle position and derived-speed estimates from dashcam video for different numbers and spatial distributions of tracked features that mimicked the continuum between barren environments and feature-rich environments. We used video from a dashcam mounted in a vehicle undergoing straight-line emergency braking. The surrounding environment had abundant trackable features on both sides of the road, including road markings, streetlights, signs, trees, and buildings. We first created a reference solution using SynthEyes, a 3D camera- and object-tracking program, and all available features. We then created eight test conditions by eliminating the available 2D trackers and 3D control points in large volumetric regions to mimic less feature-rich environments. To compare these different conditions, we calculated the bias, uncertainty, and mean absolute error in the camera position and speed estimates between the test conditions and the reference condition. For six of the eight test conditions, we found that removing large regions of the available trackers and control points generated camera solutions with relatively small position errors (mean absolute errors of 32 to 119 mm) and small speed errors (<1 km/h) compared to the reference solution. One test condition did not reach a solution and another test condition with a poor distribution of few control points had larger errors. Overall, these findings showed that the camera-tracking methodology used here for calculating a dashcam’s position and speed remained robust even in some relatively feature-poor environments.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Openalex Percentile: Top 94%
Autonomous Vehicle Technology and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.