Automatic Visual Fingerprinting for Indoor Image-Based Localization Applications
Bibliographic record
Abstract
Indoor localization has been an active research area in the last decade. Various localization systems have been proposed based on different types of signals, including but not limited to WiFi, ultrawideband, inertial measurements, and visual signals. Fingerprinting-based methods are among the most popular methods due to their accuracy and ease of deployment. However, a disadvantage to fingerprinting-based methods is the training phase in which fingerprints have to be collected at known locations and stored for future localization inquiries. Recently a few methods have been proposed to alleviate this problem. In this paper, we focus on the possibility of reducing the burden of the training phase for visual (image-based) indoor localization systems by proposing a system that automatically generates the image-location database. The proposed system will be referred to as automatic visual fingerprinting that can be paired with any indoor image-based localization method. We will also validate the proposed system through extensive experiments.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".