Bibliographic record
Abstract
Location awareness of people inside commercial establishments can help with occupancy-based dynamic energy management and indoor navigation. In this paper, we propose MobiCeil, a novel phone-based indoor localization technique. The proposed technique is offline, automated, and uses image captured from phone's camera to identify the unique ceiling structure of any particular location in the office building. The proposed method is based on these assumptions: (a) in office, employees tend to keep their phones lying on the table, and (b) the layout of ceiling landmarks in a portion of the ceiling structure (as captured by the phone's camera on the table) is unique. We validated these assumptions by checking the phone placement of 47 employees randomly at their cubicle or meeting room, and collecting ceiling layout data from 18 meeting rooms and 6 cubicles in an IT office building. To evaluate the performance of MobiCeil, we collected images of the ceiling as seen by the phone (front and back) camera in three different rotations of the phone placed on the table, to capture a total of 960 ceiling images. Our approach achieved an accuracy of 88.2% for identifying locations, with a low computation time of 2.8s per image.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".