Forensic reconstruction of an incident scene using rigid body photogrammetry techniques
Bibliographic record
Abstract
Forensic biomechanics is used to draw conclusions about incident and injury reports, relying on images of the incident for relevant photogrammetric measurement techniques, such as spatial resection and intersection. However, these techniques rely on the quality and type of media available, which can vary substantially. As such, this study aims to quantify the error associated with utilizing various supporting media. A simulated incident scene containing 2 objects of interest, a model rifle and a motorcycle, was 3D scanned and recorded from 3 camera angles. PhotoModeler was used to measure the 3D location of these objects with supporting media being limited to the use of a 3D scan, calibrated or uncalibrated cameras, single or multiple viewing angles, and stationary or moving cameras. The results of statistical analysis demonstrated that, when supported by a scan, single and multiple camera angles resulted in similar positional measurement errors. Mean errors of 6.52 cm and 5.98 cm for the single view, compared to ranges of 3.73–5.71 cm and 2.56 – 13.74 cm with multiple views, were found for the motorcycle and rifle, respectively. Also, using 3 stationary cameras resulted in lower distance and orientation errors than 3 frames from a moving camera. Thus, it was concluded that supporting 3D scans provide the highest level of accuracy and the use of single or multiple stationary cameras demonstrated higher accuracy compared to mobile cameras. Using 3D scans in conjunction with stationary cameras provides reliability and admissibility of photogrammetry-based evidence in forensic investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".