VideoLoc: Video-based Indoor Localization with Text Information
Bibliographic record
Abstract
Indoor localization serves an important role in various scenarios such as navigation in shopping malls or hospitals. However, the existing technology is usually based on additional deployment and the signals suffer from strong environmental interference in the complex indoor environment. In this paper, we propose video-based indoor localization with text information (i.e. "VideoLoc") without the deployment of additional equipment. Videos taken by the phone carriers cover more critical information (e.g. logos in malls), while a single photo may fail to capture it. To reduce redundant information in the video, we propose key-frame selection based on deep learning model and clustering algorithm. Video frames are characterized with deep visual descriptors and the clustering algorithm efficiently clusters these descriptors into a set of non-overlapping snippets. We select keyframes from these non-overlapping snippets in terms of the cluster centroid that represents each snippet. Then, we propose text detection and recognition with the perspective transformation to make full use of stable and discriminative text information (e.g. logos or room numbers) in keyframes for localization. Finally, we obtain the location of the phone carrier via the triangulation algorithm. The experimental results show that VideoLoc achieves high precision of localization and is robust to dynamic environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".