3 - Estimation robuste pour la détection et le suivi par caméra
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When designing a Driving Assistance System based on lane-markings detection, the robustness of the outputs is a crucial issue. Moreover, to be integrated into complex control systems, they must be accompanied with some confidence measure. Formulating road markings detection as the problem of estimating the parameters of a lane model, using features extracted from the image by an original procedure, we can benefit from the robustness of M-estimators and consider as a natural confidence measure the covariance matrix of the estimate. After revisiting M-estimators in an original, Lagrangian formalism, we propose two parametric families of noise models, that allow a continuous transition between Gaussian and non-Gaussian models. Then, we focus on several points of major practical importance, though seldom addressed in computer vision, such as the estimation of noise parameters and the definition of the approximate covariance matrix. We experimentally show that the accuracy of the covariance matrix depends much more on the tuning of parameters than the one of the estimation itself. A new approximation of the covariance matrix, less sensitive to the noise model, is proposed. Finally, we exhibit new matrices, faster to compute, that might be used with advantages in many other applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it