The marauder's map or the use of non-intrusive range laser scanners in the context of smart rooms
Bibliographic record
Abstract
Dans cette présentation, je vais expliquer comment des capteurs laser peuvent être utilisés pour réaliser différentes fonctionnalités importantes pour les environnements intelligents. Je montrerai comment créer une carte précise et y représenter le mouvement de toutes les personnes, en particulier les trajectoires de leurs pieds. Celles-ci peuvent servir à identifier la personne observée, car chacune a sa propre démarche. Ceci ouvre des voies dans les domaines de la domotique, des environnements intelligents et de la sécurité. Les trajectoires de pieds ont également de nombreuses applications dans le domaine médical, en particulier pour la gériatrie, la kinésithérapie et la neurologie, ce que je détaillerai. Je démontrerai également que cette technologie permet de détecter les situations de piggybacking et de tailgating. Tout ceci étant rendu possible par une chaîne de traitement de signal minutieusement étudiée et par des techniques d'apprentissage automatique.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".