PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Analysis of video recordings in accident reconstruction

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Selected methods of quantitative analysis of video recordings are presented, which can be used to analyse images from both fixed cameras (highways, intersections, etc.) and vehicle-mounted cameras. The article deals with the use of video recordings in the reconstruction of road traffic accidents. Many drivers use digital video recorders (DVRs), the so-called dashboard cameras, which record the situation in front of or behind the car while driving. There are also many places where cameras are installed, such as highways, intersections, etc. In some situations, such recordings can be important evidence in establishing liability for a road traffic accident. However, in most of these cases, the video recording is only analysed qualitatively, while the article shows that a lot of quantitative information can also be obtained from the video recording, such as speeds, accelerations and directions of movement of the vehicles. Analysing the image of the camera moving with the vehicle is more difficult, but possible thanks to the analysis methods presented in the article. The reconstruction of a road traffic accident event using the presented methods can be carried out on the basis of recordings made with the help of recording devices that capture images of different quality. It is not necessary to know the parameters of the camera recording the image. However, knowing these parameters makes the analysis much easier. In addition, reference was made to the problems of image analysis that experts have to deal with when reconstructing accidents. It was pointed out that video recordings should be analysed using different methods depending on the situation they represent. The influence of the quality of the recording (resolution, distortion, image sharpness, recording speed, etc.) on the usefulness of the recording for obtaining quantitative information is also discussed. Finally, a method for estimating the uncertainty of the results is presented. The article confirms that it is possible to determine selected parameters of vehicle movement based on the analysis of the DVR recorder.
Rocznik
Strony
127--143
Opis fizyczny
Bibliogr. 29 poz., fot., rys., wykr., wzory
Twórcy
  • Faculty of Automotive and Construction Machinery Engineering , Warsaw University of Technology, Warsaw, Poland
  • Faculty of Automotive and Construction Machinery Engineering , Warsaw University of Technology, Warsaw, Poland
Bibliografia
  • [1] Abramowski, M. (2018). Application of data video recorder in reconstruction of road accidents. In 2018 XI International Science-Technical Conference Automotive Safety, IEEE 1-6. https://doi.org/10.1109/au tosafe.2018.8373327.
  • [2] Abramowski, M. M. (2015). Analysis of the possibility of using video recorder for speed assessment of vehicle before the accident. Proceedings of the Institute of Vehicles, 4(104), 87-98.
  • [3] Adamová, V. (2020). Dashcam as a device to increase the road safety level. Proceedings of CBU in Natural Sciences and ICT, (1), 1-5. https://doi.org/10.12955/pns.v1.113.
  • [4] Behbahaninia, A., Banifateme, M., Azmayesh, M. H., Naderi, S., Pignatta, G. (2022). Markov and monte carlo simulation of waste-to-energy power plants considering variable fuel analysis and failure rates. Journal of Energy Resources Technology, 144(6), 062101. https://doi.org/10.1115/1.4051760.
  • [5] Dao, D.V., Adeli, H., Ly, H.B., Le, L.M., Le, V.M., Le, T.T., Pham, B.T.: A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation, Sustainability. 12 (2020) 830. https://doi.org/10.3390/su12030830.
  • [6] Dashboard Camera Market Size Analysis Report 2021-2028. Market Analysis Report https://www.grandviewresearch.com/industry-analysis/dashboard-camera-market Report ID: 978-1-68038-771-1 - last accessed - july 2021
  • [7] Ding, J., Yan, Z., We, X. (2021). High-accuracy recognition and localization of moving targets in an indoor environment using binocular stereo vision. ISPRS International Journal of Geo-Information, 10(4), 234. https://doi.org/10.3390/ijgi10040234.
  • [8] Diovannini, E., Giorgetti, A., Pelletti, G. (2021). Importance of dashboard camera (Dash Cam) analysis in fatal vehicle-pedestrian crash reconstruction. Forensic Sci Med Pathol 17, 379-387. https://doi.org/10.1007/s12024-021-00382-0.
  • [9] Dwivedi, R., Gangwar, S., Saha, S., Jaiswal, V. K., Mehrotra, R., Jewariya, M., Sharma, P. (2021). Estimation of Error in Distance, Length, and Angular Measurements Using CCD Pixel Counting Technique. MAPAN, 36(2), 313-318. https://doi.org/10.1007/s12647-021-00463-z.
  • [10] Epstein, B., & Westlake, B. G. (2019). Determination of vehicle speed from recorded video using reverse projection photogrammetry and file metadata. Journal of forensic sciences, 64(5), 1523-1529. https://doi.org/10.1111/1556-4029.14053.
  • [11] Guzek, M. (2018). Vehicle motion reconstruction based on EDR/ADR records – simulation research. In IOP Conference Series: Materials Science and Engineering, 421(3). https://doi.org/10.1088/1757- 899X/421/3/032011.
  • [12] Guzek, M., Lozia, Z. (2021). Are EDR Devices Undoubtedly Helpful in the Reconstruction of a Road Traffic Accident. Energies, 14(21), 6940. https://doi.org/10.3390/en14216940.
  • [13] Kopencova, D., & Rak, R. (2020, October). Issues of vehicle digital forensics. In 2020 XII international science-technical conference AUTOMOTIVE SAFETY (pp. 1-6). IEEE, Jung, Jiheon and Cho, Seong-je and Han, Sangchul and Park, Minkyu, Automotive Digital Forensics Through Data and Log Analysis of Vehicle Diagnosis Android Apps. Available at SSRN: https://doi.org/10.2139/ssrn.4579305.
  • [14] Kukheon, L., Jong-Hyun, C., Jungheum, P., Sangjin, L. (2021). Your car is recording: Metadata-driven dashcam analysis system, Forensic Science International: Digital Investigation, 38. https://doi.org//10.2139/ssrn.4579305.
  • [15] Lee, R. L., Wong, G. M., Wong, S. Y., & Koh, A. C. (2020). Use of Singapore’s “Standard Details of Road Elements” for distance estimation in traffic crash reconstruction: a comparison with onsite measurements and Google Earth Pro. Forensic science international, 313, 110260. https://doi.org/10.1016/j.forsciint.2020.110260.
  • [16] Li, L., Deng, Z. Q., Li, B., Wu, X. (2013). Fast vision-based pose estimation iterative algorithm. Optik, 124(12), 1116-1121. https://doi.org/10.1016/j.ijleo.2012.03.018.
  • [17] Martschinke, J., Martschinke, J., Stamminger, M., Bauer, F. (2019). Gaze-dependent distortion correction for thick lenses in HMDS. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 1848-1851. https://doi.org/10.1109/VR.2019.8798107.
  • [18] Okarma, K., Lech, P. (2008). Monte Carlo based algorithm for fast preliminary video analysis. In Computational Science – ICCS 2008: 8th International Conference, Kraków, Poland, June 23-25, 2008, Proceedings, Part I 8 (pp. 790-799). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-69384- 0_84.
  • [19] Olewiński, M., Perzyński, T., Pietruszczak, D. (2018). Wybrane zabezpieczenia pojazdów samochodowych przed kradzieżą. Autobusy-Technika, Eksploatacja, Systemy Transportowe, 226(12), 168-177. https://doi.org/10.24136/atest.2018.377.
  • [20] Outay, F., Abdullah Mengash, H., Adnan, M., (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges, Transportation Research Part A: Policy and Practice, 141, 116-129. https://doi.org/10.1016/j.tra.2020.09.018.
  • [21] Qasim, A. J., Din, R., Alyousuf, F. Q. A. (2020). Review on techniques and file formats of image compression. Bulletin of Electrical Engineering and Informatics, 9(2), 602-610, https://doi.org/10.11591/eei.v9i2.2085.
  • [22] Qu, S. R., Li, J., Shu, Y. (2019). Accurate vehicle location and tracking algorithms based on improved kernelized correlation motion model and Kalman filter in intelligent transport surveillance system. Journal of Ambient Intelligence and Humanized Computing, 1-10. https://doi.org/10.1007/s12652-019- 01589-4.
  • [23] Rok, K., Matjaž Nekrep, P., Darja, T., (2020). Using the scanners and drone for comparison of point cloud accuracy at traffic accident analysis, Accident Analysis & Prevention, 135. https://doi.org/10.1016/j.aap.2019.105391.
  • [24] Usmankhujaev, S., Baydadaev, S., Kwon, J. W. (2023). Accurate 3D to 2D Object Distance Estimation from the Mapped Point Cloud Data. Sensors, 23(4), 2103. https://doi.org/10.3390/s23042103.
  • [25] Verolme, E., Mieremet, A. (2017). Application of forensic image analysis in accident investigations. Forensic science international, 278, 137-147. https://doi.org/10.1016/j.forsciint.2017.06.039.
  • [26] Wach, W. (2014). Metoda Monte Carlo w analizie zdarzeń drogowych, a interpretacja wyników obliczeń. The Archives of Automotive Engineering – Archiwum Motoryzacji, 66(4), 83-106.
  • [27] Wach, W., Unarski, J. (2020). Analysis of video recordings using the light board: stabilization of timing, high-speed camera, exposure time. In 29th Annual Congress of the European Association for Accident Research (EVU).
  • [28] Yao, Y., Xu, M., Wang, Y., Crandall, D. J., Atkins, E. M. (2019). Unsupervised Traffic Accident Detection in First-Person Videos. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 273-280. https://doi.org/10.1109/IROS40897.2019.8967556.
  • [29] Zhang, X., Lu, Z., Cheng, K., Wang, Y. (2020). A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(4), 622-635. https://doi.org/10.1177/1748006X1989950.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1fc2ea75-990d-4058-aed5-b20b8f2597e9
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.