A Method of Estimating the Distance and Relative Speed of an Object when a Vehicle Hits It, as Indicated in the Black Box Image Created at the Moment of Impact
Myung Cheol Park*, Jong Hyuk Kim, Won Taek Oh and Sang Hyun Lim
Traffic Accident Analysis Division, National Forensic Service, Republic of Korea
Submission:April 1, 2020; Published:April 13, 2020
*Corresponding author:Myung cheol Park, Traffic Accident Analysis Division, National Forensic Service, Republic of Korea
How to cite this article:Myung Cheol P, Jong Hyuk K, Won Taek O, Sang Hyun L. A Method of Estimating the Distance and Relative Speed of an Object when a Vehicle Hits It, as Indicated in the Black Box Image Created at the Moment of Impact. J Forensic Sci & Criminal Inves. 2020; 13(5): 555873. DOI: 10.19080/JFSCI.2020.13.555873.
Abstract
This paper proposes a method by which the distance and relative speed of a vehicle vis-à-vis the object it hit are measured using the phenomenon in which when a vehicle impacts another vehicle, a pedestrian, or another object, if the object is n times distant from the black box, its image looks about n times smaller
Keywords:Forensic video; Car black box; Vehicle speed; Pixel; Distance; Relative speed; Impact; Collision
Method of Measuring the Distance and Relative Speed of an Object Hit by a Vehicle from the Images Createdby the Vehicle’s Black Box
a) Method of measuring the object filmed by the vehicle’s black box
Figure 1 shows the method of measuring the distance of a tree filmed by a black box. The tree with 3 length was expected to be located 30 from the black box The aforementioned can be attributed to the fact that resembles , and as such, DODEAOAB=resembles ÄDOFand ÄAOCThus,//DOAOFOCO=. Therefore, //DEABFOCO=, so3//10hhFOm=, and FO is 30m. Figure 2 shows the method of measuring the distance of a stick filmed by the black box. At ()0ttt=−Δtime, the reference stick was moved so that its distance from the black box could be assumed as00dns=, and the reference stick was extended so that its extended length could be assumed as0hhnh=, with the top and bottom of the imaginary stick touching the imaginary lines. Then, 000hdnsns=and 0hhnh= 00hdsh∴ was expected. In the above expression, can be attributed to the fact that the black box image magnification may vary according to the distance, and to the possibility of magnification may vary according to the distance, and to the possibility of distortion [1-4]. In addition, it was assumed that after the black box image was captured at , the pixel of 0h would be 0p. Next, it was assumed that the pixel of captured from the black box image at was , and that the pixel of 0h was p, and 0pnp=.
Then, 00hdsh, 00phhp, and 0pnp=.
The distance d of the reference stick moved from the black box was estimated using the expression below.
b) Method of Measuring the Distance and Relative speed of a Pedestrian and the Vehicle ahead Filmed by the Black Box
Figure 3 & 4 shows the method of measuring the distance and relative speed of a pedestrian filmed by the black box. If the pedestrian’s distance from the black box is and the distance between the vehicle and the pedestrian is , then 0sds=−. Therefore, the distance is expected [5-9].
Further, if, at , the relative speed of the vehicle and the pedestrian is , is expected in the expression below




Conclusion
This study proposed the following [10-14]. If, at the time when a vehicle hits an object, the horizontal distance from the vehicle black box to the part of the object that was hit is assumed as reference distance ; the pixel length of the part of the object that was hit is assumed as ; and at the time of rewinding the black box, the part of the object that was hit is assumed as , the expression
References
- I Han (2016) Car speed estimation based on cross-ratio using video data of car mounted camera (black box). Forensic Sci Int 269: 89-96.
- T Wong, C0 Tao, Y Cheng, K Wong, C Tam (2014) Application of cross-ratio in traffic accident reconstruction. Forensic Sci Int 235: 19-23.
- G Edelman, J Bijhold (2010) Tracking people and cars using 3D modeling and CCTV. Forensic Sci Int 202: 26-35.
- JLan, J Li, G Hu, B Ran, L Wang (2014) Vehicle speed measurement based on gray constraint optical flow algorithm.Optik 125:289-295.
- D Jeyabharathi, D Dejey (2016) Vehicle tracking and speed measurement system (VTSM) based on novel feature descriptor: diagonal hexadecimal pattern(DHP). J Vis Commun Image R 40: 816-830.
- NC Mithun, T Howlader, SM Rahman (2016) Video based tracking of vehicles using multiple time-spatial images.Expert Syst Appl 62: 17-31.
- JSochor, R Juranek, A Herout (2017) Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement. Comput Vis Image Underst 161: 87-98.
- Jong Hyuk Kim, Won Taek Oh, Ji Hun Choi, Jong Chan Park (2018) Reliability verification of vehicle speed estimate method in forensic videos. forensic Sci Int 287: 195-206.
- R Fay, R Robinette, D Deering, J Scott (2002) Using Event Data Recorders in Collision Reconstruction. SAE 1: 0535.
- JLawrence, C Wilkinson, B Heinrichs, G Siegmund (2003) The Accuracy of Pre-Crash Speed Captured by Event Data Recorders. SAE 1: 0889.
- C Wilkinson, J Lawrence, B Heinrichs, D King (2005) The Accuracy and Sensitivity of 2003 and 2004 General Motors Event Data Recorders in Low Speed Barrier and Vehicle Collisions. SAE1: 1190.
- R Brown, L Lewis, B Hare, M Jakstis, R Landis, et al. (2010) Confirmation of Toyota EDR Pre-Crash Data. SAE 1: 0998.
- HC Gabler, DJ Gabauer, HL Newell, ME O Neill (2004) Use of Event Data Recorder (EDR) Technology for Highway Crash Data Analysis.Annual Research Report of NCHRP.
- GA Nystrom (2010) Analysis of Multi-Vehicle Rear-End Accidents.SAE 1: 0055.