Suspect Height Estimation Using the Faro Focus<sup>3D</sup> Laser Scanner
Bibliographic record
Abstract
At present, very little research has been devoted to investigating the ability of laser scanning technology to accurately measure height from surveillance video. The goal of this study was to test the accuracy of one particular laser scanner to estimate suspect height from video footage. The known heights of 10 individuals were measured using an anthropometer. The individuals were then recorded on video walking along a predetermined path in a simulated crime scene environment both with and without headwear. The difference between the known heights and the estimated heights obtained from the laser scanner software were compared using a one-way t-test. The height estimates obtained from the software were not significantly different from the known heights whether individuals were wearing headwear (p = 0.186) or not (p = 0.707). Thus, laser scanning is one technique that could potentially be used by investigators to determine suspect height from video footage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".