Skip to main content
SpringerLink
Log in

The study on the estimation of vehicles speed using a dashboard camera

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Mobile and handheld electronic products such as smartphones and dashboard cameras are frequently used to record occurrences of traffic accidents nowadays. Most existing studies require at least one entire lane marking as a reference to calculate a certain amount of driving distances and are not adapted to a targeted vehicle with a rapidly changing speed in a short time. Therefore, existing approaches cannot provide the desired accuracy of speed estimation in real-life scenarios. In this study, we obtain dynamic time and space data from recorded footage using dashboard cameras and then apply photogrammetry and cross-ratio methods to estimate the vehicle speed. In addition, the proposed method is applied to other cars' speed estimation and ego-speed estimation even though both cars are moving. The experimental results show that given the frame rate on the recorded footage, our proposed method only needs one object in each frame to estimate the vehicle speed even at a rapidly changing speed. Our proposed method shows that the difference between the estimated speed and the reference speed by the Global Positioning System (GPS) is smaller than 1 km/h when only one car is on the move, and smaller than 3 km/h when both cars are on the move. Nevertheless, the difference between the estimated speed and the reference speed is between 1.46 km/h and 5.28 km/h when one car moves by AUPD method. To our knowledge, there is no existing method that estimated the front vehicle speed when both cars are on the move using dashboard cameras.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from Wen-Chao Yang but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Wen-Chao Yang (una135@mail.cpu.edu.tw). For replicability, we make the cross-ratio calculation function in an Excel file available to the public and could be downloaded from https://dtcloud.cpu.edu.tw/index.php/s/8uxSU2BRQ2KaTj7.

Abbreviations

GPS:

Global Positioning System

CCTV:

Closed-circuit television

VSEM:

Vehicle speed estimate method

DVR:

Digital video recorder

EDR:

Event data recorder

MPH:

Miles per hour

3D:

Three-dimensional

2D:

Two-dimensional

NIST:

The National Institute of Standards and Technology

SWGIT:

The Scientific Working Group on Imaging Technology

SWGDE:

The Scientific Working Group on Digital Evidence

PUPD:

The pictorial unit pixel distance

AUPD:

The actual unit pixel distance

References

  1. Klein LA, Mills MK, Gibson DR (2006) Traffic detector handbook: Vol I (No. FHWA-HRT-06–108). Turner-Fairbank Highway Research Center, Accessed 5 October 2022. https://www.fhwa.dot.gov/publications/research/operations/its/06108/06108.pdf

  2. Krishnan R (2008) Travel time estimation and forecasting on urban roads. Dissertation, Centre for Transport Studies, Imperial College London. https://doi.org/10.1080/23249935.2016.1151964

  3. Weber N, Moedl S, Hackner M (2002) A novel signal processing approach for microwave Doppler speed sensing. In : 2002 IEEE MTT-S Int Microw Symp Dig 3:2233–2235. https://doi.org/10.1109/MWSYM.2002.1012317

    Article  Google Scholar 

  4. Park SJ, Kim TY, Kang SM, Koo KH (2003) A novel signal processing technique for vehicle detection radar. IEEE MTT-S Int Microw Symp Dig 1:607–610. https://doi.org/10.1109/MWSYM.2003.1211012

    Article  Google Scholar 

  5. Zou J, Yan P, Tian Y, Wei L (2018) A novel vehicle velocity measurement system based on laser ranging sensors. In: 2018 IEEE Int Conf Applied Syst Invent (ICASI) 613–616. https://doi.org/10.1109/ICASI.2018.8394329

  6. Hoogeboom B, Alberink I (2010) Measurement uncertainty when estimating the velocity of an allegedly speeding vehicle from images. J Forensic Sci 55(5):1347–1351. https://doi.org/10.1111/j.1556-4029.2010.01412.x

    Article  Google Scholar 

  7. Kim JH, Oh WT, Choi JH, Park JC (2018) Reliability verification of vehicle speed estimate method in forensic videos. Forensic Sci Int 287:195–206. https://doi.org/10.1016/j.forsciint.2018.04.002

    Article  Google Scholar 

  8. Shim KS, Park NI, Kim JH, Jeon OY, Lee H (2021) Vehicle speed measurement methodology robust to playback speed-manipulated video file. IEEE Access 9:132862–132874. https://doi.org/10.1109/ACCESS.2021.3115500

    Article  Google Scholar 

  9. Epstein B, Westlake BG (2019) Determination of vehicle speed from recorded video using reverse projection photogrammetry and file metadata. J Forensic Sci 64(5):1523–1529. https://doi.org/10.1111/1556-4029.14053

    Article  Google Scholar 

  10. Adnan MA, Zainuddin NI (2013) Vehicle speed measurement technique using various speed detection. In: Instrumentation, IEEE Bus Eng Industrial Applications Colloq (BEIAC). https://doi.org/10.1109/BEIAC.2013.6560214

  11. Liu S, Yang X, Cui J, Yin Z (2017) A novel pixel-based method to estimate the instantaneous velocity of a vehicle from CCTV images. J Forensic Sci 62(4):1071–1074. https://doi.org/10.1111/1556-4029.13381

    Article  Google Scholar 

  12. Modenov PS, Parkhomenko AS (2014) Projective Transformations: Geometric Transformations, vol 2. Academic Press, New York and London

    Google Scholar 

  13. Yang L, Luo JC, Song XW, Li ML, Wen PW, Xiong ZX (2021) Robust vehicle speed measurement based on feature information fusion for vehicle multi-characteristic detection. Entropy 23(7):910. https://doi.org/10.3390/e23070910

    Article  Google Scholar 

  14. Yang L, Li QY, Song XW, Cai QJ, Hou CP, Xiong ZX (2021) An improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion. Entropy 23(7):866. https://doi.org/10.3390/e23070866

    Article  MathSciNet  Google Scholar 

  15. Bouziady AE, Thami ROH, Ghogho M, Bourja O, Fkihi SE (2018) Vehicle speed estimation using extracted SURF features from stereo images. In: 2018 Int Conf Intell Syst Comput Vision (ISCV), 17737798. https://doi.org/10.1109/ISACV.2018.8354040

  16. Wong TW, Tao CH, Cheng YK, Wong KH, Tam CN (2014) Application of cross-ratio in traffic accident reconstruction. Forensic Sci Int 235:19–23. https://doi.org/10.1016/j.forsciint.2013.11.012

    Article  Google Scholar 

  17. Milne JJ (2015) an elementary treatise on cross-ratio geometry: with historical notes, scholar’s choice, pp 10–11

  18. Brannan DA, Esplen MF, Gray JJ (2011) Geometry. Cambridge University Press

    Google Scholar 

  19. Han I (2016) Car speed estimation based on cross-ratio using video data of car-mounted camera (black box). Forensic Sci Int 269:89–96. https://doi.org/10.1016/j.forsciint.2016.11.014

    Article  Google Scholar 

  20. Kumar A, Khorramshahi P, Dhar P, Chellappa R, Lin WA, Chen JC (2018) A semi-automatic 2D solution for vehicle speed estimation from monocular videos. In: IEEE/CVF Conf Comp Vision Pattern Recognit Workshops (CVPRW). https://doi.org/10.1109/CVPRW.2018.00026

  21. Rais AH, Munir R (2021) Vehicle speed estimation using YOLO, Kalman filter, and frame sampling. In: 2021 8th Int Conf Advanced Inform: Concepts, Theory Applications (ICAICTA). https://doi.org/10.1109/ICAICTA53211.2021.9640272

  22. Lin CJ, Jeng SY, Lioa HW (2021) A real-time vehicle counting, speed estimation, and classification system based on virtual detection zone and yolo. Math Probl Appl Syst Innovations IoT Appl 2021:1577614. https://doi.org/10.1155/2021/1577614

    Article  Google Scholar 

  23. Prajwal, Navaneeth, Tharun, Kumar A (2022) Multi-vehicle tracking and speed estimation model using deep learning. In: 2022 14th Int Conf Contemporary Comput. 258–262. https://doi.org/10.1145/3549206.3549254

  24. Farid A, Hussain F, Khan K, Shahzad M, Khan U, Mahmood Z (2023) A fast and accurate real-time vehicle detection method using deep learning for unconstrained environments. Appl Sci 13(5):3059. https://doi.org/10.3390/app13053059

    Article  Google Scholar 

  25. SWGDE (2022) SWGDE Technical Notes on FFmpeg, Accessed 5 October 2022. https://drive.google.com/file/d/1OH9tNLeZ3_aqXlVJsUNB6nr3SR1ULR74/view

  26. SWGIT (2022) Guidelines for Image Processing, Accessed 5 October 2022. https://www.crime-scene-investigator.net/swgit-section5.pdf

  27. Su KA (2021) The study on evaluating vehicle speed with digital images. Central Police University, Taiwan, ROC, Thesis

    Google Scholar 

  28. Jenkins FA, White H E (2001) Fundamentals of optics, 4th ed. McGraw-Hill Education, pp 171–173

Download references

Acknowledgements

This work on this paper was supported by the Ministry of the Interior, Republic of China (Taiwan). (Project No. 112-0805-02-28-01).

Author information

Authors and Affiliations

  1. Department of Forensic Science, National Central Police University, Taoyuan, Taiwan, Republic of China

    Wen-Chao Yang

  2. Electrical and Computer Engineering Department, Old Dominion University, Norfolk, VA, USA

    Jiajun Jiang & Chung-Hao Chen

  3. Ocean Lakes High School, Virginia Beach, VA, USA

    Austin Mao

  4. Forensic Science Center, New Taipei City Police Department, Taipei, Taiwan, Republic of China

    Kai-An Su

Authors
  1. Wen-Chao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiajun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Austin Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kai-An Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chung-Hao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen-Chao Yang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Competing interests

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or nonfinancial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, WC., Jiang, J., Mao, A. et al. The study on the estimation of vehicles speed using a dashboard camera. Multimed Tools Appl 83, 45777–45798 (2024). https://doi.org/10.1007/s11042-023-17171-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17171-2

Keywords

Search

Navigation