A Method for Automatic Image Rectification and Stitching for Vehicle Yaw Marks Trajectory Estimation

  • Vidas Žuraulis Vilnius Gediminas Technical University
  • Dalius Matuzevičius Vilnius Gediminas Technical University
  • Artūras Serackis Vilnius Gediminas Technical University
Keywords: accident reconstruction, image alignment and stitching, critical speed equation, yaw marks, vehicle trajectory,

Abstract

The aim of this study has been to propose a new method for automatic rectification and stitching of the images taken on the accident site. The proposed method does not require any measurements to be performed on the accident site and thus it is frsjebalaee of measurement errors. The experimental investigation was performed in order to compare the vehicle trajectory estimation according to the yaw marks in the stitched image and the trajectory, reconstructed using the GPS data. The overall mean error of the trajectory reconstruction, produced by the method proposed in this paper was 0.086 m. It was only 0.18% comparing to the whole trajectory length.

Author Biographies

Vidas Žuraulis, Vilnius Gediminas Technical University
Department of Automobile Transport, Traffic Safety Laboratory
Dalius Matuzevičius, Vilnius Gediminas Technical University
Department of Electronic Systems
Artūras Serackis, Vilnius Gediminas Technical University
Department of Electronic Systems

References

Srinivasal R, Persaud B, Eccles K, Carter D, Baek J.

Safety Impacts of Signing Delineation for Horizontal

Curves on Rural Two-Lane Roads. Journal of Transportation.

;3:55-66.

Monajjem MS, Kamali MHJ, Ayubirad MS. Studying the

Effect of Spiral Curves and Intersection Angle on the Accident Rates on Two-Lane Rural Highways in Iran. Promet – Traffic & Transportation. 2013;25(4):343-348.

Levulytė L, Žuraulis V, Sokolovskij E. The research of dynamic characteristics of a vehicle driving over road roughness. Maintenance and reliability = Eksploatacja i niezawodność. 2014;16:518-525.

Yang J, Ma R, Zhang Y, Zhao Ch. Sliding mode control for trajectory tracking of intelligent vehicle. Physics Procedia. 2012;33:1160-1167.

Žuraulis V, Sokolovskij E, Matijošius J. The opportunities

for establishing the critical speed of the vehicle on research in its lateral dynamics. Maintenance and reliability = Eksploatacja i Niezawodność. 2013;15:312-318.

Hsu L, Chen T. Vehicle dynamic prediction systems with on-line identification of vehicle parameters and road conditions. Sensors. 2012;12:15778-15800.

Sokolovskij E, Prentkovskis O. Investigating traffic accidents:

the interaction between a motor vehicle and a pedestrian. Transport. 2013;28:302-312.

Onofrio JA, Howarth LE. Forensic Engineering Review of the Critical Speed Formula. National Academy of Forensic Engineers Journal. 2005;22.

Brach RM. An analytical assessment of the critical speed formula. SAE. 1997:1-9.

Fraser C, Cronk S, Hanley H. Close-range photogrammetry in traffic incident management. In Proceedings of XXI ISPRS congress commission V, WG V, Citeseer. 2008; 1:125-128.

Du X, Jin X, Zhang X, Shen J, Hou X. Geometry features measurement of traffic accident for reconstruction based on close-range photogrammetry. Advances in Engineering Software. 2009;40(7):497-505.

Bernat K, Tokarczyk R. Automation of Measurements of Selected Targets of Photopoints in Application to Photogrammetric

Reconstruction of Road Accidents. Geomatics and Environmental Engineering. 2013;7:15-23.

Paliska D, Batista M, Starin R, Fabjan D. An Attempt to Attain New Information in Reconstruction of Road Traffic Accidents Applying Digital Image Processing. Promet – Traffic & Transportation. 2011;23(2):113-119.

Batista M, Magister T, Bogdanović L. Computer based road accident reconstruction experiences. Promet – Traffic & Transportation 2012;17(2):65-75.

Randles B, Jones B, Welcher J, Szabo T, Elliott D, Mac-

Adams C. The accuracy of photogrammetry vs. handson measurement techniques used in accident reconstruction. No. 2010-01-0065. SAE Technical Paper; 2010.

Wang YW, Lin Ch. A line-based skid mark segmentation system using image-processing methods. Transportation Research Part C. 2008;16:390-409.

Neale WT, Hessel D, Terpstra T. Photogrammetric measurement error associated with lens distortion. SAE Technical Paper. 2011-01-0286:1-54.

Apeltauer T, Macur J, Holcner P, Radimsly M. Validation of Microscopic Traffic Models Based on GPS Precise Measurement of Vehicle Dynamics. Promet – Traffic & Transportation. 2013;25(2):157-167.

PC-Rect, A Photograph Rectification Program, Operating & Technical Manual. Linz, Austria; 2013. 89 p. [20] Wang YW. A tire mark localization method for forensic image analysis. Journal of the Eastern Asia Society for Transportation Studies. 2007;7:2881-2890.

Lambourn RF, Jennings PW, Knight I, Brightman T. New and Improved Accident Reconstruction Techniques for Modern Vehicles Equipped with ESC Systems. Published project report; 2007. 42 p.

Wang YW, Lin Ch, Wu J. Skidmark Patterns and Identification

of ABS-Equipped Passenger Car. Journal of the Eastern Asia Society for Transportation Studies. 2005;6:3401-3412.

Nurkhaliesa BH, Halim S, Zulkepli M. Reconstruction of Traffic Accident Scene Using Close-Range Photogrammetry Technique. Geoinformation Science Journal. 2010;10(1):17-37.

Published
2016-02-24
How to Cite
1.
Žuraulis V, Matuzevičius D, Serackis A. A Method for Automatic Image Rectification and Stitching for Vehicle Yaw Marks Trajectory Estimation. PROMET [Internet]. 2016Feb.24 [cited 2019Dec.15];28(1):23-0. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1752
Section
Articles