Inter‐observer variation of head and foot point selection for subject height determination
Bibliographic record
Abstract
The purpose of this study was to examine factors affecting video analysts' decisions in marking the vertex of the head and foot point and corresponding inter-observer marking variances when conducting height analysis on individuals seen in video. Nineteen video analysts participated in an exercise at the 2022 Ontario Forensic Video Analysts' Association (OFVAA) conference where they were asked to mark the vertex of the head and a corresponding foot point of a "suspect" on extracted video frames in a variety of positions and with different headwear (no headwear, baseball cap, and hoodie). A height scale with discrete marking points located at the same positions as where the suspect was positioned was also included in a separate image set, offering a comparison to the suspect. Marked points for all analysts were overlayed onto the respective image frame for visual observations. Summary statistics were used for data interpretation. This study demonstrated that factors such as the suspect's proximity to the camera and suspect's headwear affected the variability and range of marking, which has a direct correlation to the estimated height of the suspect. In general, when the region to be marked was larger, the variability was also larger. This study also demonstrates that marking errors were significantly reduced when discrete marking locations were present such as on a height scale. The average percentage difference of height was most notable, approximately 3%, when the suspect was wearing a hoodie and was at a position closest to the camera. The range of the percentage difference was also the highest at this position, which was 10.6%. In comparison, the height scale had a maximum percent height difference of 0.6% at position D-5, the furthest position from the camera. The range at this location was approximately 2%, which was also the highest range value for the height scale. Future studies should consider suspect posture and look at how these errors may be minimized by examining the best locations to mark the head and foot points under different scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".