Robust Body-Height Estimation for Applications in Automotive Industry
Bibliographic record
Abstract
An automatic adjustment of the seat position according to the driver height significantly increases the level of comfort when entering a car. A camera attached to a vehicle can estimate the body heights of approaching drivers. However, absolute height estimation based on a single camera leads to several problems. Cost-sensitive cameras used in automotive industry provide low-resolution grayscale images, which make driver extraction in real-life parking scenarios difficult. Absolute height estimation also prerequisites a known camera position relative to a road surface, but this position is not available for any parking scenarios. Toward this, we first propose a background-based driver-extraction method that can operate on low-resolution grayscale images, and that is robust against shadows and illumination changes. Second, we derive a scheme for estimating the camera position relative to an unknown road surface using head and foot points of extracted persons. Our experimental results obtained from real-life video sequences show that the proposed schemes are highly suitable for robust driver extraction and height estimation in automotive industry.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".