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Vehicle Speed Measurement Based on Camera Calibration

Published: 06 October 2018 Publication History

Abstract

Road traffic injuries cause considerable economic losses to individuals, their families, and to nations as a whole. Every year the lives of more than 1.25 million people are cut short as a result of a road traffic crash. A common cause of accidents is driving faster than one can stop within their field of vision. Measuring a vehicle's speed using video data is an important technique in determining the cause of a traffic accident. When on-site distance measurement cannot be carried out for a video recorded by a surveillance camera, the speed of a vehicle moving along a straight line in the video can be measured by selecting 2 reference points on the body of the vehicle and computing the speed of the vehicle during the time when the part of the vehicle between the reference points passed an environment point. The 2-reference-point method is widely used in traffic accident investigations in China. However, in real-world applications it often lacks high accuracy. In this paper we propose an improved method based on camera calibration. The proposed method selects 4 coplanar points on the body of the vehicle and computes their world coordinates by camera calibration. The speed is measured based on the movements of the 4 points. Experiments confirmed that the proposed method outperforms the 2-reference-point method.

References

[1]
World Health Organization | Road Traffic Injuries. http://www.who.int/mediacentre/factsheets/fs358/en/.
[2]
Leibowitz, Herschel W.; Owens, D. Alfred; Tyrrell, Richard A. The assured clear distance ahead rule: implications for nighttime traffic safety and the law. Accident Analysis & Prevention. 30 (1): 93--99, 1998.
[3]
Jeong U T, Yeong-Tae O, Park Y S, et al. A Study on Correlation Between Skid Distance and Pre-Braking Speed{J}. Its World Congress, 29(3), 2011.
[4]
Navin F. Truck braking distance and speed estimates {J}. Canadian Journal of Civil Engineering 13(4):412--422, February 2011.
[5]
Wood D P, Glynn C, Walsh D. Estimation of the collision speed in a collision of a motorcycle or scooter with a car from individual vehicle deformation {J}. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, 228(3):295--309, 2014.
[6]
Paduraru G. Vehicle speed correlation with deformation amplitude due to adult pedestrian impact in car traffic accidents {J}. Ovidius University Annals of Mechanical, Industrial and Maritime Engineering, Volume X, Tom I, 2008.
[7]
Tan H, Zhang J, Feng J, et al. Vehicle Speed Measurement for Accident Scene Investigation {C}. IEEE International Conference on E-Business Engineering, 389--392, 2010.
[8]
Xu S, Yang S, Chen C, Wu G. Speed Identification Based on Surveillance Video in Traffic Accidents{C}. International Conference on Intelligent Control and Computer Application, 2016.
[9]
He L. The Method of Video Vehicle Speed Identification Based on the Direct Linear Transformation{J}. Science Technology and Engineering 17(19):172--176, 2017.
[10]
Han I. Fuzzy estimation of vehicle speed in pedestrian collision accidents {J}. International Journal of Automotive Technology, 14(3):385--393, 2013.
[11]
Hoxha G, Shala A, Likaj R. Pedestrian crash model for vehicle speed calculation at road accident {C}. International Journal of Civil Engineering and Technology 8(9):1093--1099, September 2017.
[12]
Du L, Sun Q, Bai Y, et al. Uncertainty evaluation for field experimental standard of vehicle speed-measuring devices in actual traffic {C}. International Symposium on Precision Engineering Measurement and Instrumentation. International Society for Optics and Photonics, 2015.
[13]
Happer A, Araszewski M, Toor A, et al. Comprehensive Analysis Method for Vehicle/Pedestrian Collisions{C}. SAE 2000 World Congress, 2000.
[14]
Yilmaz A C, Aydin K. Impact Velocity Prediction in a Traffic Accident{C}. MATEC Web of Conferences 81:02003, 2016.
[15]
Chinese industry standard GAT1133-2014 for vehicle speed identification based on video {S}.
[16]
Feng H, Pan S, Chen J, Speed Determination Using Video Recording{J}. Chinese Journal of Forensic Sciences, 2009(5):46--48, 2009.
[17]
Ju Z, Wang C, He X. Vehicle speed detection algorithm research of real-time video image{J}. Application Research of Computers, 34(9):2822--2824, 2017.
[18]
Wu Q, Research on speed identification based on video and wheelbase {J}. Journal of Hubei University of Police, 27(7):168--169, 2014.
[19]
Ao Hui Yun. Study on speed evaluation methods for vehicles involved in road accidents based on various ways{D}. East China Jiaotong University, 2014.
[20]
Li G, Yang Z, He X. Speed identification methods in traffic accidents{J}. Transpo World, 2012(11):130--131, 2012.
[21]
Zhuang P, Ma J. Application of video in speed computation for vehicles in traffic accidents{J}. Technology and Economic Guide, 2017(4).
[22]
Hartley R, Zisserman A. Multiple View Geometry in Computer Vision {M}. Cambridge University Press. ISBN 0-521-54051-8. 2003.
[23]
Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11):1330--1334, Nov 2000.
[24]
Wang Z, et al. Automatically measuring the coordinates of streetlights in vehicle-borne spherical images. Journal of Image and Graphics 23(09), 2018.
[25]
Zeng F Y, Zhong R F, Song Y, et al. Vehicle panoramic image matching based on epi-polar geometry and space forward intersection{J}. Journal of Remote Sensing, 18(6): 1230--1236, 2014.

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    ICGSP '18: Proceedings of the 2nd International Conference on Graphics and Signal Processing
    October 2018
    119 pages
    ISBN:9781450363860
    DOI:10.1145/3282286
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Griffith University
    • City University of Hong Kong: City University of Hong Kong

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    Published: 06 October 2018

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    Author Tags

    1. camera calibration
    2. reference-point method
    3. traffic accident investigation
    4. vehicle speed measurement

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