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SAE TECHNICALPAPER SERIES 2001-01-3313

Determining Crash Data Using Camera Matching Photogrammetric Technique

Stephen Fenton, William Neale, Nathan Rose and Christopher HughesKnott Laboratory, Inc.

Reprinted From: ATTCE 2001 ProceedingsVolume 1: Safety

(P-367)

Automotive & Transportation TechnologyCongress & Exhibition

October 1- 3, 2001Barcelona, Spain

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2001-01-3313

Determining Crash Data Using Camera MatchingPhotogrammetric Technique

Stephen Fenton, William Neale, Nathan Rose and Christopher HughesKnott Laboratory, Inc.

ABSTRACT

Accident scene photographs contain importantinformation that can be useful in determining howaccidents happened. However, dimensions are difficultto gather from photographs. The size of an object in thephotographs depends on how far away from the camerathe object is located. An object in the background lookssmaller and will measure smaller than the same sizeobject in the foreground. This phenomenon is calledperspective distortion.

Photogrammetry was introduced in the late 1800’s as atool to compensate for the perspective distortion andassist in gathering dimensions from photographs. Oneof the early techniques was to create a transparentminiature of a photograph and place the miniature in theview screen on the camera. The camera was then takento the scene and matched to the correct position suchthat the image in the scene matched the image in theview screen. Today, using computer modeling software,a scene can be created in the computer model thatmatches the actual photograph. Using a techniquecalled camera matching, the camera in the computer canbe adjusted to match the photograph. Once properlymatched, dimensions within the photograph can begathered. This technique is useful in gatheringdimensional data from crash scene photographs like thepoint of impact and the point of rest of crash vehicles.Once the crash scene dimensions are determined, theaccident can be reconstructed using the principals ofconservation of momentum and energy.

INTRODUCTION

In the late 1980’s, a photogrammetric technique calledthe reverse projection method was introduced1,2. Thismethod was based on re-establishing the originalcamera viewpoint by returning to the scene with atransparency placed in a camera’s view screen andviewing the scene through the transparency. A Nikon F-3 camera with a detachable view prism was designed sothat the transparency could be placed in the view screen.At the scene, one person would position the camera and

adjust the focal length so that the transparency matchedthe scene. A second person would mark the position ofthe lost features such as the tire marks and positions ofthe vehicles. Once these features were determined, theywould be surveyed to determine the necessary crashdata. This method of reverse projection was tedious andtime-consuming making it difficult to accomplish atscenes with heavy traffic. Due to the high cost of thecamera, and the time/safety issues, this process was notoften used.

A safer and more cost effective solution has arisen usingcomputer modeling software. Instead of creating atransparency of the scene, the scene can be recreatedusing computer modeling software. A virtual camera canbe created in the computer model. The scenephotograph can be digitized and placed in the virtualcamera’s view port. In the computer model, the cameracan be adjusted so that the modeled scene matches thebackground image. Once the camera is properlymatched, the necessary dimensions can be identifiedand measured.

DISCUSSION

PROCEDURE

Below is an outline of the steps involved in gatheringdimensions from photographs using the cameramatching photogrammetric technique.

1. Create a digital model of the scene.• Gather dimensional data of known objects by

either obtaining an aerial photograph of thescene or by performing a scene inspection.

• Using scene data, create three-dimensionaldigital computer model of the accident scene.

2. Import scene photographs into the computer.• Digitize photographs.• Calibrate photographs as background images in

the computer modeled scene.3. Camera match the digital model to the background

images.

Copyright © 2001 SAE International and Messe Düsseldorf.

• Create a camera in the computer model for thebackground image. A guideline for placement isto consider the height of the photographer.Place the camera approximately five feet fiveinches above the ground level. Start with a 50mm lens. Based on the observed discrepanciesbetween the perspective of the photograph andperspective of the computer scene, adjust thefocal length and vanishing point of the cameraaccordingly. If the objects look stretched whenmatched to the photograph, increase the focallength and move the vanishing point away. If theobjects look squished, decrease the focal lengthand bring the vanishing point closer.

4. Testing Accuracy of Camera Position• Measure other objects in the scene and

compare their dimensions to the knowndimensions of the objects.

CASE STUDY

To explain the process in greater detail, these authorscreated an accident scene and photographed thevehicles and the skid marks. The locations of thevehicles and skid marks were surveyed. Our objectivewas to determine the distances between the skid marksand vehicles using the camera matchingphotogrammetric technique and compare the results tothe survey.

Figure 1 – Original Photograph

The first step was to gather dimensions from the sceneso that a 3D model of the accident scene could be made.This can either be done by visiting the scene or, in thiscase, by gathering the data from an aerial photograph.The locations of the lane lines and curbs were measuredand documented from the aerial. This information wasused to create a three-dimensional model of the accidentsite in AutoCAD3. The data was then imported into a 3D-modeling program called 3DStudio Max4.

Figure 2 – CAD Model over Aerial Photo

A virtual camera was then placed in the computermodeled scene within 3DStudio. The originalphotograph was digitized and placed as the backgroundin the camera view port. The camera was set at a heightof five feet five inches off the roadway surface and thecamera’s focal length was set at 50mm. The camera’slocation was moved throughout the computer modelwhile attempting to match the three-dimensional modelto the photograph in the background.

As mentioned previously, there were three dimensionsthat the camera could be moved (x, y, z), however the zdimension was known relatively well because we had agood idea of the photographer’s eye height. The cameracould also be rotated about the x, y, and z-axis. Theposition and rotational values were relatively easy toadjust, however the camera’s focal length was the mostdifficult parameter to adjust correctly. When selectingthe virtual camera settings, it is recommended that a50mm lens be selected initially. A 50mm lens mostreasonably represents what the human eye sees.However, often times the camera used may have had azoom lens. If you are able to contact the photographer todetermine the setting, this could save you some time.Through trial and error, you will notice that as thecamera’s focal length is changed from 50mm to 28mm,objects in the scene become distorted. In this case, thelane stripes became shorter and did not match up withthe lane stripes in the photograph in the background. Asthe focal length was changed from 50mm to 80mm, thelane lines became longer and still did not match upcorrectly. After modifying the focal length and adjustingthe camera’s position and rotation, the computer modeleventually matched the photographic background. Aftersome practice, this process became easier.

Figure 3 – CAD Model Matched to Photograph

The next step in the process was to import scaledmodels of the vehicles into the scene and to position themodels so that they matched the photograph in thebackground.

Figure 4 – CAD Model with Vehicles Added

Boxes with the same exterior dimensions (length, widthand height) were used to represent the vehicles. Thevehicles were positioned in their correct location at thepoints of rest. The tire marks were traced on the roadwayso that they matched the photograph in the background.Once the vehicles and tire marks were recreated, thescene could be viewed from the top to determinedistances.

Figure 5 – Top View of CAD Model

In this case, we were interested in the length of the skidmarks and the locations of the vehicles at the point ofimpact and rest. The photograph clearly showed thepoint of rest of the vehicles, and the point of impact wasdetermined by making copies of the vehicles and placingthem at the end of the tire marks. The red Honda wasposition so that its front tires lined up with the end of theskid marks. This was the red Honda’s point of impactposition. The black Audi was placed so that the damageto the right side matched the front of the red Honda.This was the black Audi’s point of impact position.

Figure 6 – Top View with Vehicles added at Point of Impact

In this case, we identified the start of the right skid markas Pt. A and the end as Pt. B. We identified the start ofthe left skid mark as Pt. C and the end as Pt. D. Theblack Audi’s left rear tire location was identified as Pt Eand the front left tire was identified as Pt F. The redHonda’s right rear tire was identified as Pt G, and thefront right tire was identified as Pt H. A dimensionalanalysis was performed comparing the actual

dimensions gathered in the field to the dimensionsdetermined in the camera matching analysis. Theseauthors found that the dimensions determined throughthe camera matching process were on average within2.2% of the field dimensions.

Point ID FieldDimension

Camera MatchedDimension

PercentDifference

A-B 59.76 61.56 3.01%A-C 13.64 12.91 5.35%A-D 60.12 61.42 2.16%A-E 80.58 83.02 3.03%A-F 77.08 78.47 1.80%A-G 124.09 123.38 0.57%A-H 128.32 128.96 0.50%B-C 72.63 73.86 1.69%B-D 5.13 5.42 5.65%B-E 47.86 48.27 0.86%B-F 39.54 39.35 0.48%B-G 64.63 61.98 4.10%B-H 69.51 68.11 2.01%C-D 72.61 73.38 1.06%C-E 93.83 95.49 1.77%C-F 90.58 91.19 0.67%C-G 137.17 135.82 0.98%C-H 141.60 141.57 0.02%D-E 52.86 53.72 1.63%D-F 44.49 44.74 0.56%D-G 65.35 63.14 3.38%D-H 70.75 69.80 1.34%E-F 8.74 9.14 4.58%E-G 70.39 69.33 1.51%E-H 69.43 69.16 0.39%F-G 64.73 63.56 1.81%F-H 64.67 64.81 0.22%G-H 8.63 9.34 8.23%Average 2.12%

CONCLUSION

Using this camera matching photogrammetric techniqueenables dimensions to be gathered from photographsquickly and safely using typical computer modelingsoftware. The process provides a very descriptive andcompelling visual record that can be used to gatherimportant crash data. Based on the case study, theresults are well within the levels of accuracy that makethis process useful, although the accuracy depends onthe ability of the user to accurately place the camera inthe correct position with the correct focal length. Thissame limitation is inherent in the reverse projectionmethod also.

REFERENCES1 Woolley, R., White, K., Asay, A., and Bready, J.;“Determination of Vehicle Crush from Two Photographsand the Use of 3D Displacement Vectors in AccidentReconstruction”; Society of Automotive Engineers -910118, 1991.

2 Smith G., Allsop D.; “A Case Comparison of Single-Image Photogrammetry Methods”; Society of AutomotiveEngineers - 890737, 1989.

3 AutoCAD 2000, Autodesk, San Rafael, California.

4 3DStudio Max, Discreet, a Division of Autodesk. SanRafael, California.

CONTACT

The authors can be reached at the following address:Knott Laboratory, Inc.7185 South Tucson WayEnglewood, CO 80112USAPhone: (303) 925-1900Fax: (303) 925-1901

Email: sfenton@knottlab.comhttp://www.knottlab.com/